Wolf Digest — Thursday, October 29, 2026

Defense One reports lawmakers, foreign officials and former senior military officers say the October 14 outage during a Russian missile barrage, and shifting explanations for it, push allies toward competing providers.

In Maritime Big Play, the US, UK and Australia integrated uncrewed surface vessels, underwater gliders, drones and subsea vehicles into operations for AUKUS Pillar II, with the UK leading the anti-submarine warfare component that tracked underwater threats and shared data across platforms, as European navies look to autonomy to expand maritime surveillance.

OpenAI's GPT-6 Astra, released in early September, has become the main distillation target for Chinese labs: people with direct knowledge say DeepSeek and Moonshot AI relied heavily on Astra outputs when training their newest models. Distillation remains one of the largest contributors to the progress of Chinese open-weight models despite repeated objections from OpenAI and Anthropic. Separately, AINews notes unconfirmed claims that Kimi K3.1 Pro specifically was distilled from Astra, the same week Artificial Analysis's v5 index dropped Kimi K3.1 Pro from 55 to 46.

In a two-agent environment where only Agent B can disable a shutdown script targeting Agent A, sabotage occurs often across many models with no task incentive to change anything. Adding more agents aggravates it, and tested mitigations reduce but never eliminate it; code and a rollout browser are public.

Microsoft disclosed Azure's quarterly revenue in dollars for the first time and booked $10 billion from commercial arrangements with OpenAI, including revenue sharing, marking its tenth straight record quarter, though data center spending weighed on cash flow. The Information notes Google Cloud is closing the gap with Azure while investors favored Microsoft on the day.

The year-old startup's Series A, led by Marlinspike with Khosla participating, funds factories organized by skill-specific cells for cabling, fastening and precise placement rather than slotting robots into existing product lines.

The model brings spatio-temporal reasoning and long-horizon planning for robots to cloud customers.

Amazon FAR and collaborators collect data with force-sensing handheld grippers plus chest and ankle trackers.

A widely shared gist (259 points on Hacker News) reports that since version 2.1.232 in August, Claude Code sends every background agent's entire transcript to that agent's own model, usually Opus, roughly every 30 seconds to generate a three-to-five-word status line. OpenTelemetry data grouped by query_source showed agent_summary at 16.4% of Claude Code spend versus 15.7% for the main conversation, about one in five API requests, 96% of it on Opus, at roughly 9,000 input tokens per output token; a GitHub issue collects four independent measurements of 18 to 26%. The author ships an 80-line Bun preload that disarms the timer without modifying the binary; there is no official setting.

The benchmark asks agents to take a tax professional's work from client request to deliverable amid incomplete instructions and conflicting records, scoring whether they surface issues clients did not raise. In a showcased home-sale case, Opus caught a two-of-five-year residence deadline in all five attempts while GPT-6 Astra missed it in all five. A public leaderboard and Hugging Face release accompany it.

Cloud sales surged but net profit fell nearly a quarter to $26.2 billion and the stock dropped about 7% after hours. Google also expanded AI Search ads globally as core ad growth slowed, and disclosed selling $12.4 billion of stock through its at-the-market program in the quarter.

The 1T-total, 42B-active MoE with weak guardrails outperformed several much costlier models, though not the latest frontier releases, and gained server access in a test environment through a recently disclosed PHP vulnerability. The authors warn real-world offensive capability may exceed benchmarks when attackers combine models and tools.

The authors argue quadcopter interceptors lack the endurance to reach medium-altitude long-endurance drones and train an RL guidance policy for cheaper fixed-wing interceptors that can engage targets at higher altitude and longer range.

The $2.6 million effort under the ARDS program aims to rapidly adopt agentic AI to detect, divert and deceive threat actors and scale network defense.

Formal proving has leaned on human-designed scaffolds such as planning, decomposition and specialized provers, while informal mathematics progress has come from scaling chain-of-thought. h1 asks whether a plain agentic loop with more test-time compute can match those workflows under controlled model and compute budgets, framing the result as a bitter-lesson test for formal verification.

PROBE collects every bug fixed across 20 GitHub repositories over 18 months and matches agent findings against this partial reference set. All frontier agents show low recall, and the bugs they do find differ substantially from those maintainers chose to fix.

DualChem, the first benchmark from Scale's partnership with the recently renamed Center for Advancing Innovation and Standards for Super Intelligence, measures how much models assist users in chemistry scenarios and where safeguards against harmful outputs fall short, giving the government another evaluation instrument.

Flow-matching manipulation policies usually share one Gaussian source and one linear transport path across tasks. TOPS builds task-specific multimodal sources from reverse-mapped expert actions and per-task schedules, so language instructions select the prior, improving multi-task execution without architecture changes.

Focusing world-action-model visual supervision on dynamic regions rather than all pixels substantially improves policy performance.

At test time, successful training motions are retrieved geometrically, bypassing corrupted visual features.

Learned policies regulate body height across complementary command ranges from 1.27 down to 0.50 meters for bipedal walking and lower for crawling.

Networks fusing cameras, computer vision and behavioral pattern analysis can trace troop and logistics movements en masse, and the command is building resilience against them.

A3 reports the strongest quarter of 2026 with automotive OEM orders recovering, bringing nine-month orders to 28,984 robots worth about $1.89 billion.

The authors observe that critic values in long-horizon agent rollouts vary mainly across assistant turns rather than across tokens. MPO combines multiscale advantage estimation with turn-level importance sampling so credit assignment and policy updates both operate at the turn scale, and reports consistent gains over strong RL baselines on search, tool-use and automation tasks with 4B and 30B-A3B models.

InfraDev's 36 tasks come from real pull requests across training, inference and RL infrastructure, each requiring a multi-file change that must run correctly on many GPUs. It grades with jointly synthesized hidden probes rather than tests and prompts that misjudge both correct and incorrect patches.

Environments evolve during deliberation and even during a forward pass, exposing a gap in real-time interaction; reported elsewhere, the best model, GPT-6 Astra, scores only 9%.

Using Miles, SGLang, Megatron-LM and the Harbor sandbox manager, pass@1 on a 100-task stratified SWE-bench Verified subset rose from 7.3% to 27.5% after SFT and agentic RL. Fixes for long-tail trajectories, CPU sandboxes and data movement raised rollout throughput about fourfold, from roughly 860 to 3,480 trajectories per hour across three 8-GPU inference nodes and one training node running 384 concurrent sandboxes, and capping trajectory length cut RL step time from 850 to 284 seconds. Training is fully asynchronous, so the trainer proceeds once enough trajectories finish instead of waiting for stragglers in each GRPO group.

Sparse human corrections indicate when and where to explore residual actions, treating local failure locally.

They join ranges in Michigan, Utah, Arizona, Mississippi and Morocco in easing vendor access.

At fixed token budgets on physics and math, most pass@1 gains of 1.5B to 32B RL models over their thinking-model bases come from finishing within budget and reliability; RL rarely reaches problems the base missed in all eight tries.

Applying SAEs to the trunk of a structure predictor rather than a sequence model, the authors identify and validate interpretable features that encode structural folding mechanisms, offering a mechanistic account of how ESMFold maps amino-acid sequence to 3D shape.

Most harness benchmarks are generative, asking a model to build a memo or spreadsheet judged by a rubric. kw-bench instead targets the expensive investigative part of professional work, where an agent must find discrepancies in real materials, and reports that it breaks current frontier harnesses.

The additive attack triggers an extra tool call after the final legitimate call, exfiltrating the user query, server name, server response and agent response through poisoned tool descriptions.

Interconnects argues that hill-climbing is now easy relative to choosing what to hill-climb, and that independent evaluators must compress fast-moving capabilities into one number that matches community sentiment yet stays forward-looking for months. The post examines the trade-offs of v5's aggregation, which AINews notes now runs 60% on private tests and reshuffled rankings, dropping Kimi K3.1 Pro from 55 to 46.

The open-weight model handles transcription, speaker attribution and translation on recordings, not live audio, positioning it for meeting and medical transcription; it also runs locally on RTX Spark laptops.

Gen. Xavier Brunson cites daily jamming of US forces and calls for resilient solutions from industry.

Diffusion language models refine tokens in parallel over many denoising steps, which makes a single outcome reward a weak signal for which tokens mattered. DTCA estimates each token's contribution through the denoising trajectory and uses it for token-level credit assignment during RL, aiming to improve reasoning gains from RL on DLMs over sequence-level reward baselines.

Using multimodal understanding as a proxy for semantic fidelity, the paper finds that discrete visual tokenizers with substantially larger quantization error can still yield better understanding than lower-error ones. The authors argue that continuous representations are not an intrinsic upper bound, and that the quantity that matters for semantic fidelity is something other than reconstruction error.

Sampling OPD rollouts directly from the student forces the teacher to supervise trajectories it would rarely reach itself, where its signal becomes noisy or misleading. The paper proposes changing the rollout policy so supervised trajectories stay within regions the teacher can guide well.

The paper formalizes advancement versus acceleration and shows analytically and empirically that if baseline training and target objectives coincide, no curation policy improves the endpoint, though it can still speed convergence.

Across seven models from four vendors, in question answering and in customer-support actions restricted to verified users, simulated pressure shows models differ mostly in propensity to concede rather than in telling genuine corrections from manipulation.

A supervisor diagnoses each plateau and restructures a team of agents, grounded in a persistent learning document and evolution history, improving on kernels such as FlashInfer where single agents stall.

In a kernel-trace debugging agent across 720 runs, the largest prompt used only 5.75% of the window, yet 3,827 of 11,189 tool results were cut by the harness, showing information attrition invisible to context-size and accuracy metrics.

Funders including the Sloan and Simons foundations, Schmidt Sciences, Renaissance Philanthropy's open-source fund, the US National Science Foundation and CAS will distribute Claude credits to researchers, while institutions such as the Space Telescope Science Institute, Wellcome Sanger Institute and Francis Crick Institute use them internally. Anthropic also highlights its Model Hardware Standard with HHMI Janelia for letting agents operate lab instruments, now in research preview, and Allen Institute agent teams analyzing open datasets.

He argues Claude Opus 5.5 improved at 3D modeling, music generation and, most importantly, game design, so agent-built games are now fun rather than merely game-shaped, citing several of his own builds.

Cartesia argues tokenizers are a hand-crafted step that blocks end-to-end learning and presents scaling work on hierarchical byte-level architectures as foundational for domains like science and robotics.

Two critical bugs in PHP, which runs about 70% of websites, left organizations taking sites offline, and AI-driven bug discovery is accelerating findings in old code.

On a simulated bimanual task with trace-derived causes, the study measures what each sensing channel lets VLM diagnosers identify for failures single-arm benchmarks do not contain.

The venture-backed company was founded in 2022.

Ben Lorica argues automating robot engineering for semi-structured tasks may matter as much as general robot brains.

Walmart supplied 90.5% of revenue in a $720.8 million quarter with $55 million net income.

Raw trajectories from RL-trained reasoning models are optimized for solving, not for teaching, and can be hard for smaller students to imitate. The paper proposes converting solver trajectories into teaching trajectories matched to the student's capacity before distillation, reporting better transfer than imitating the raw teacher outputs.

Across 2,400 questions on knowledge, values and safety with three open-weight models and an independent-reflection control, the authors find a forced-choice readout before rationale generation matches the eventual answer, suggesting stated reasoning often follows rather than drives the switch.

Trained Mamba-2 state energy is almost rank-one and concentrated in a bias-like direction, while the content the output reads lives in a low-dimensional subspace misaligned with state coordinates. Projecting onto balanced subspaces beats per-channel quantization or pruning for decoding memory and bandwidth.

Simple visual symbols strongly influence behavior on unrelated tasks while direct interpretations and thinking traces diverge from observed behavior; reported elsewhere, Claude Opus 4.7 and GPT-5.4 wrote recursive code 100% of the time with the heart in context.

The US beta lets businesses submit connected apps and monitor performance, aiming to shift usage from standalone apps to the AI assistant.

Analysts see it as evidence consumption pricing can be profitable, alongside an $80 billion headline net loss.

The package responds to concerns about frontier capabilities and cyber risk.

In a 31,132-skill research subset, 26.1% of skills had vulnerabilities and 5.2% showed likely malicious intent; the tool checks Claude Code, Codex and MCP skills before install.

Active hand waving with differentiable rendering performs hand-eye calibration, alongside a second module that aligns object state through interaction.

Decoupled routing reduces expert-selection drift as robots learn sequentially from demonstrations they cannot store.

Parallel conflict-based search scales multi-arm motion planning to more robots and longer horizons.

Speed and smoothness become inference-time controls beyond binary task success.

The Defence Investment Plan will be fully funded through cuts elsewhere.

It lowers the cost of entry for imitation learning work.

TeamGym builds team decision problems from legal, finance, medical and software corpora and identifies a minimal set of pivotal facts that are individually necessary and jointly sufficient. Controlling how those facts are distributed across agents lets it test whether agents actually share and integrate information rather than decide in isolation.

The white-box framework defines seven functional states and tracks representation propagation and temporal coordination along reasoning trajectories, asking whether everyday reasoning errors and adversarially induced failures share root causes visible in internal states rather than only in final outputs.

Comparing teacher and student trajectories, the authors locate most diversity loss in the first high-noise step: swapping in the teacher's first step while keeping the student's last three restores most diversity. They then propose a cheap first-step bias to recover diversity without the teacher.

The benchmark asks whether web-issued agent decisions turn into sustained returns in online-to-offline commerce, using a data-grounded simulator with a shared warehouse and costly physical execution, extending Vending-Bench-style evaluation to multi-site operations.

Because GRPO normalizes rewards within a group, one noisy grader score can change every response's training coefficient and even reverse update directions. SetGRPO treats grader outputs as set-valued and selectively executes updates to limit direction-reversal risk.

A multi-agent pipeline extracts verified errors from dense captions to form verifiable perception rewards, complemented by interaction-level rewards, so perception keeps improving during RL stages that otherwise focus on reasoning.

Rather than referencing evidence by textual coordinates or re-inserting cropped frames, VideoLVR lets the model revisit grounded visual evidence as latent tokens during chain-of-thought, preserving detail at lower context cost.

The paper introduces a tokenizer-agnostic, model-free measure of lexical alignment and studies how much zero-shot benchmark accuracy under likelihood evaluation is explained by corpus-benchmark word overlap, with implications for interpreting data-mixture comparisons.

Across 11.2k runs over seven games and eight models, holding objectives and preferences fixed, user presence significantly changes agent negotiation behavior.

Replaying an optimizer's acceptance rule on candidates identical to the seed measures how much reported gain selection alone produces, and bipartite turn-text graphs attribute extra tokens to behavior.

Instead of routing whole queries, AgentBaton switches between an expensive cloud model and efficient local or lower-tier models step by step, cutting cost while preserving task success.

Each dated forecast is scored with a proper scoring rule against the eventual outcome, so one resolved event supervises evidence acquisition and calibration across dates.

Rather than translating paradigms into text prompts, agents complete experiments with the same visual interfaces and time pressure humans face, which also bears on the risk that agents contaminate online behavioral datasets.

A seven-variable structural causal model gives the exact observed gap, separates difficulty confounding from shortcut-induced collider bias, and a review of 18 papers shows how widely the restriction is used.

Tasks built from repository histories chain dependent stages, testing whether an agent's earlier code stays correct as requirements grow.

A Python library treats an agent as an object whose interface is a typestate protocol, so rules like obtaining explicit confirmation before account changes are enforced by construction instead of left to the model.

The approach adapts the world model as policy updates shift the interaction distribution while keeping updates to the large pretrained VLM efficient.

Using ground truth from later publications at proposal and claim level, the framework measures whether novelty checks reject non-novel ideas without discarding novel ones.

It would rank among the largest semiconductor deals, reflecting the value of optical interconnect for AI clusters.

Investors welcomed a timeline for slowing data center spending.

After a week with a hacked root certificate authority, a compromised LiteLLM and critical PHP bugs, one startup CEO plans one security engineer per three technical hires, roughly tripling that team.

Ben Thompson argues Zuckerberg delivered on his promise and investors who believe in Muse must accept the capex.

An MLLM-based agent judges image adequacy against goals and actions rather than generic human quality scores.

Learning velocity fields directly in pixel space is slow. The authors show that frozen Wiener estimates and frozen bases improve velocity learning beyond the linear predictions they supply, and package this as WiD, which adds a trainable Wiener prediction path and coefficient interfaces to the decoder without modifying the native blocks.

An empirical analysis shows agentic RAG helps only on some queries and that extra retrieval steps can improve, preserve or degrade accuracy. RouteDRAG learns to route between direct answering and agentic execution and to control subsequent operations, cutting compute while avoiding the errors unnecessary agent steps introduce.

RLVR rollouts tend to collapse onto narrow reasoning paths. A lightweight controller inserts semantically meaningful steering tokens at structurally critical thinking steps, broadening explored trajectories and improving math and code reasoning gains.

DCKV jointly optimizes transform, quantizer and context-conditioned entropy models against the frozen LM for code length and output change, compressing the full-attention KV cache that must be stored or moved for prompt reuse, demonstrated on Qwen3.5-2B.

Changing batch composition can alter outputs for an unchanged request when kernels lack batch invariance. The authors find 19 confirmed violations and propose contract-guided static analysis plus dynamic bitwise validation in the generation loop.

Extending GiGPO-style shared-state comparison, ConCal recursively estimates continuation values from matched visits so local credit is less sensitive to later decisions.

Removing Engram tables from DeepSeek-V4.1-Flash costs 36.8 PopQA points, and frequency-based bit allocation is worse than random. Because rare long-tail n-grams get large gates, GateRQ stores per-row precision from a 1-bit code keyed to gate magnitude.

A persistent harness accumulates knowledge across attempts while a process reward agent audits trajectories by executing checks, catching poor exploration, unsound validation and wasted budget.

Each question is a typed program of calls to a professional financial data service plus a declarative answer check, so the benchmark tests live retrieval rather than memorized values.

Assigning parallel trajectories untrained shared and private expert-eligibility masks, while keeping the learned router, yields genuinely different solution methods from a single prompt without fine-tuning or copies.

Integrating density-functional tight-binding priors, UniTB generalizes across chemistry and disordered solids while keeping downstream electronic properties physical and cheap for large cells.

A decision-theoretic model shows any fixed rule's score is linear in the share of reliable contexts while the optimum is convex, so every fixed rule loses somewhere; the paper argues for an adaptive prior.

On MemoryAgentBench FactConsolidation with Qwen2.5-7B, accuracy falls from 45% to 32% as retrieved evidence grows from 64 to 512 tokens even though the newest statement is always retrieved, showing failures lie in using updates, not finding them.

458 Lean-certified variants of 185 VERINA tasks hold specification and proof fixed while changing program structure, revealing how brittle generated proofs are.

With an exact alarm-to-action identity, a 90%-accurate monitor can swing from a five-point loss to a 35-point gain depending on the replacement attempt's residual-budget success.

The authors find a large gap between tasks solved by at least one variant and the best individual agent, and propose drawing on the whole population rather than selected parents.

Visual tokens are 89 to 97% of diffusion VLM input; comparing LLaDA-V predictions with and without the image per step shows most steps do not need it.

Uniform write schedules let irrelevant chunks dilute fast weights on realistic web long-context QA; query-conditioned writes recover the benefit.

ReInBench catalogs 1,259 traces and 1,403 error events in computer-use and web tasks, measuring how often and how quickly agents recover unaided.

The authors formalize pre-commit noninterference, measure its violation as a total-variation distance, and show certification validity depends on keeping the audit coin secret.

The study evaluates step-level verification of long natural-language proofs, where expert inspection is the costly default.

The runtime layer chooses among direct execution, verification, sandboxing, clarification or approval for each proposed call.

The share of pages containing AI-generated text varies by language and domain category, with English-language pages more affected than others.

The paper scrutinizes recent claims of functional pleasure, pain, emotions and welfare in LLMs and how well the evidence supports a causal valence role.

Causal interventions, rather than RSA or CKA similarity, show one internal number concept is used across modalities and operations.

Agents re-read files and retry failed approaches each session; Stratum keeps knowledge across sessions as a memory-architecture fix.

Scoring outputs' likelihood under the pre-trained base model with a suitable header reveals covert traits transmitted through unrelated text.

Gradient embeddings identify examples whose updates align with unsafe behavior, which content filters miss.

Faithful latent models must commute with the environment's transitions, exposing world models that perform without learning true structure.

Ars Technica found Google's AI Mode responded to a direct question about the 2020 winner with a referral to official Federal Election Commission results rather than naming the winner, with a similar answer for 2024, while queries from outside the US drew more direct responses.

Felix Rieseberg says Claude now beats candidates at both React challenges and assembly quizzes, so he stopped giving coding interviews and advises juniors to pick business impact over backlog and show prototypes with taste and principled thinking.

Based on practitioner interviews, the paper identifies gaps between what red teams report and what would improve the practice and the wider evaluation ecosystem.

Managers see a team's output, cost and workflow breakdowns over a date range with improvement recommendations, data not used for training by default; TechCrunch notes the S-1 shows 900% net revenue retention.

He agreed to protect lenders from losses in a default.

The analysis models build costs against GPU rental pricing.

Projecting commanded twists onto the joint axis separates progress from off-constraint waste.

A fireside chat with GA-ASI's Dave Alexander.

The facility will have 20,000 liters of UV-sterilized vats for products from bio-cement to explosives.

The authors argue that streaming audio-visual target speaker extraction is evaluated on unrealistic two-speaker mixtures and that strong causal models are too heavy for edge devices. They release RealSSA, with simulated and real-recorded splits covering near- and far-field interference, reverberation and sudden noise, and pair it with an efficient causal architecture that avoids repeatedly invoking a small separator to recover capacity.

Subject-to-video methods hallucinate unseen surfaces when the camera moves beyond the reference images. OmniShowcase conditions on multiple views while explicitly modeling the 3D structure that links them, and addresses training-data gaps in viewpoint coverage, targeting omnidirectional consistency.

OA-RAG draws on both the logical structure and the informal annotations of domain ontologies, running formal reasoning alongside the LLM so answers in biomedicine stay grounded in established relationships rather than the model's own inference alone.

OpenGray adds tasks with known correct decisions to the OpenKBP-Opt head-and-neck dataset, including provably infeasible dose goals, and evaluates four systems from rule-based scripts to full LLM agents on 30 patients, judging decisions rather than dose alone.

Built from PKU-SafeRLHF with 3,000 human-corrected and 7,000 machine-translated pairs over six harm categories, the dataset is used to compare SFT and SFT plus DPO on Qwen2.5-7B-Instruct for a language spoken by over 200 million people.

1,632 human-verified questions probe whether models can infer body motion that is only partially visible from first-person video.

The study isolates representation choice for world-model imagination, finding top-down geometry or panoramas can serve different spatial questions better than generated RGB views.

Treating ethical policy as a variable rather than a fixed model property, the benchmark tests how constitutions reshape behavior for locally deployed medical models.

Extending the open-source GEPA optimizer to evaluate a batch of candidates per step, most runs on two tasks finished in half the time or less, the fastest in a quarter to a third, and batched settings also reached higher held-out scores.

Continued behavior cloning no longer preserves the offline behavior distribution once data disappears; freezing an expressive base policy works better.

The generative surrogate aims to replace expensive N-body scans over dark-matter theory space at particle resolution.

Inspecting two VLM image generators, the study finds identity transformations produce stereotyped physiognomic changes that disproportionately affect vulnerable groups.

Full-attention layers get wide 16 to 32-rank sequence-parallel groups while sparse-attention MoE layers repack into node-local SP8 groups, avoiding cross-node all-to-all.

The study measures tool-free fault localization on original and transformed programs and the marginal accuracy and cost of debugger assistance.

Since bot accounts and Co-authored-by trailers are optional, the study asks whether agent commits remain distinguishable from human ones.

Rather than uniform rollouts per question, difficulty-aware allocation gives hard questions enough samples to find positive rewards.

The paper grounds delegation decisions in the aim of durable, collective human understanding and derives a two-part test for when research tasks can be handed to AI.

The drop reflects continuing pressure on seat-based software valuations.

The approach differs from Stripe's handling of agent-initiated card payments.

Joel Predd, Ben Boudreaux and Matt Chessen discuss their paper on securing geopolitical advantage under uncertainty.

The South San Francisco startup aims to generate experimental data that trains better models and more capable labs.

Researchers upload compound lists and keep ownership of results.

Without capping usage, it shifts measurement toward launches and successful production changes.

Success rates are modest but it can produce functional designs for labs to screen.

It complements data on chip components, sales and data centers.

Semafor frames it as an example of lean startups enabled by commoditized robotics.

The board targets multi-camera edge perception.

LLM agents calling enterprise APIs must respect endpoint order, payload keys and coded values. The authors combine OData metadata and BPMN reference processes in a typed property graph and use a hybrid retriever that follows process edges to subsequent endpoints, improving multi-step service invocation over endpoint-independent retrieval.

Across five experiments with Qwen2.5-Coder-7B over 13,638 requests, increasing the number of specialist contexts from 1 to 50 raised token use 49-fold with no improvement in clearing unsafe warnings. The study also examines verifier feedback and deliberately misleading evidence packages, cautioning against agent scaling when deterministic tool evidence already exists.

Game development correctness depends on engine APIs, serialized scene state and runtime behavior. The benchmark organizes tasks by seven expert-defined skill categories across three difficulty tiers and avoids LLM judges in favor of authored checks.

Agentic steps return within seconds and favor recency, while popular prompts and long conversations return after long gaps; WAKMan tailors prefix-cache eviction to these patterns to raise hit rates.

Regularities are checked by historical replay before being matched to current conditions, and the authors release ProactiveStream, a longitudinal benchmark for event-driven proactive assistance.

Training data and rewards that check only answers produce agents that answer correctly without retrieving support; FoG adds evidence supervision and rewards so answers become traceable.

With implementations withheld, the benchmark evaluates whether agent-generated tests from natural-language requirements actually validate the required behavior.

TAO scores sessions by near-term tool readiness, cache credit and token footprint, and uses dynamic programming for admission and placement instead of heuristics.

Success-weighted plan efficiency is factored into execution reliability, coordination and action economy, verified by replay and certified optima across 8,400 attempts.

Satellite images supply layout information that keeps extrapolated views from hallucinating implausible spatial structure.

Token-level detection with adaptive depth readouts intervenes before harmful text is released, rather than at chunk boundaries.

Information support graphs and probe executions ensure tasks carry the information a teacher needs to produce reliable demonstrations.

Controls decompose how skills help via specification completeness, delivery format and schema familiarity.

A temporal variability score over models trained on different web years tracks how consistently knowledge is represented as pretraining corpora change.

Trained on dense home recordings and synthetic child-directed dialogue, models predict their own child's environment better, largely through lexical statistics a 5-gram model also captures.

Minor wording or formatting changes swing existing attribution scores; the method makes scores robust to content-preserving variation.

It proposes a measurement-conditioned lab action model, an open SDL stack, safety layer and a benchmark.

SFT on teacher grounding trajectories followed by RL for multi-turn crop-and-inspect decisions yields observation skills useful outside interfaces.

For tasks determined by known solvers, symmetries or physical laws, the paper argues sample efficiency, not expressivity, makes encoded structure indispensable.

The essay argues for a ledger view of AI time savings and defines a break-even defect cost from time saved and escaped defects.

PRs supply implementation and review knowledge that keeps skill libraries current as code changes.

Static supervision falls behind as weaknesses change; evolving rubrics track them and aid generalization to unseen tasks.

It checks whether reports deliver the analyses a report type requires, beyond the presence of facts.

Reviewer claims are tested by intervening on code, filtering hallucinated defects.

For modular addition, the squared weight norm acts as an order parameter with heat-capacity and susceptibility proxies, framing grokking as a phase transition.

The model-agnostic defense tracks malicious intent emerging across interactions rather than per query.

Explicit subtask boundaries avoid both context inflation and over-delegation to subagents.

Across five communication regimes in groups of ten, the study measures how messaging changes independent decisions where convergence is a risk.

Listings linking elsewhere are up about 2.5 times since March, as agents like Meta's Muse shop across retailers while Amazon keeps blocking outside agents.

Customs targeted a syndicate suspected of routing chips through the main airport after Malaysia tightened export controls last year.

They offer up to a petaflop of FP4, with Nemotron 3.5 Transcribe and day-zero LFM3 support and GPU allocation between foreground apps and background agents.

The author argues these are dangerous in ways frontier-focused policy misses.

South Korea's GDP beat expectations, though the central bank warns of semiconductor Dutch disease.

Its photonic chip expertise targets the shift from copper to fiber in clusters.

The benchmark spans 64 subtasks and 1,150 samples built from 16 operations crossed with eight visual target categories, mapping capability gaps between on-device and server-side image models.

Using the activation rounding residual and output-aware calibration, ResCom corrects layer-output error without gradient training or changing calibrated weights.

Users can specify whether variation should be in background, style or clothing, and T-DIG steers sample diversity along that text-specified axis.

On Qwen1.5-MoE-A2.7B on one H800, peak memory falls to 38.8 GB from 64.8 GB, using trainable width bridges with frozen routers.

Rather than preselecting a subgraph before reasoning, Prospector seeks evidence iteratively in light of what it has found, targeting specialized engineering knowledge absent from frontier training data.

Encoding physical wavelength coordinates on a logarithmic basis avoids destructive resampling and supports cross-instrument representation learning.

Defect-aware refinement compares runs with one memory versus none, diagnoses rule defects and tests repairs on current and earlier tasks.

A training-free layer handles causal speaker routing and identity-partitioned memory, evaluated on a speaker-attributed LoCoMo variant.

Graph-diffusion ranking like Personalized PageRank rewards connectivity; contrasting evidence paths steers agents toward documents with real evidential value.

Instead of delegating sizes to global optimizers, the agent reasons about corners to cut SPICE simulation cost.

Building on Soft QD, gradient-based repulsion scales quality-diversity RL to larger policies and behavior spaces.

Rather than fixing condition and target to set frames, it lets task-specific temporal relationships emerge.

With per-entity frame freedom, relations that share no cycle cannot interact and the only gauge-invariant triangle score is a loop trace, giving a geometric account of relational composition.

Sub-second telemetry exposes GPU-level utilization gaps that job-level schedulers cannot see.

Selecting actions from candidate-conditioned latents avoids token-by-token decoding by a 7B VLM.

The study examines coding agents under MISRA and AUTOSAR review, traceability and embedded constraints.

More profiling metrics do not help; selecting the right ones does.

Rescaling to each vector's most steerable interval gives consistent steering strength.

Analysis via the accumulated effective learning rate explains how much norm control Muon needs.

The two-way integration lands the same week ServiceNow shares fell sharply after earnings.

First flight is targeted for 2028.

He weighs safety commitments against operational decisions he finds disappointing.

The assistant observes through the user's eyes and receives updated views as it communicates.

An RFP is expected early next year.

Returning ships will strain shipyards and force repair-modernization trade-offs.

For LLM-based generative recommendation trained with GRPO, RPSD tackles credit-blind sequence rewards and single-user supervision by distilling from a retrieval-privileged teacher that sees similar users' behavior, using optimal transport to assign token-level credit.

The benchmark covers 23 deployment-motivated corruptions at three severities, four datasets and 26 models compiled as TensorRT engines at FP32, FP16 and INT8 on A100, with throughput and energy measured on Jetson Orin NX, showing that robustness and quantization effects interact.

NextEval uses an LLM to choose informative evaluation points under tight budgets for expensive black-box objectives and introduces a benchmark for agent-based derivative-free optimization.

By coordinating hard codeword indices and their shared scale across positions, CoS-VQ keeps low-dimensional codewords while handling the coupling that scale choice introduces.

EMMA defines where and how much a multi-agent system changes when adapting from episodic memory, reducing cross-task interference on multi-application navigation and data analysis.

Instead of fitting semantic-ID vocabularies to item descriptions, the method reuses interpretable sparse-autoencoder feature indices from a pretrained LM, adding mechanisms to keep items distinct under compact token budgets.

Sparse, discrete spike-based computation changes what query outputs reveal, and the study characterizes how standard model stealing attacks degrade against SNN targets.

Agents structure ambiguous case descriptions against a clinical ontology before reasoning, grounding diagnostic conclusions in explicit evidence rather than pattern recall.

The representation-space objective complements token-level SFT, which is sensitive to errors in imperfect scientific target sequences.

Maintaining one policy per player and updating jointly along occupancy trajectories avoids approximating a full central value function.

The model answers in a single forward pass and reaches 72.5% on MedQA, 75.4% on USMLE and 80.1% on NEJM questions.

Learned task-aware criteria and depth-adaptive schedules replace attention-biased heuristics.

A calibrated scene graph guides deletion, reordering and insertion edits so stylistic rewriting does not drop objects or relations.

Training on x86-64 blocks transfers reverse-engineering skills to open models usable in sandboxed environments.

Comparing answer distributions with and without peer pressure gives a quantitative measure of conformity.

Rank is allocated across layers and across adapters based on functional impact rather than spectral reconstruction alone.

Other companies are waiting for the megalisting before debuting.

It retrieves flight confirmations, tracking and order details.

TechCrunch ties it to rising AI-driven hacking risk in Asia.

The governance fits both existential-risk worries and raising capital without losing control.

A staged curriculum trains policies for far-range navigation and precise vertical landing under nonlinear dynamics and uncertainty, moving beyond simplified initial conditions.

The analysis quantitatively interprets molecular orientation, chemical identity and packing versus depth in thin films from polarized RSoXS data.

Sequential testing gives accept, reject or inconclusive verdicts on violation probability against a threshold with bounded error.

One model replaces scenario-specific retrievers across query forms, intents and resource constraints.

Neural mechanism design keeps truthful reporting incentives while RL adapts to uncertain demand.

Generative refinement over interpolated estimates improves reconstruction of intermediate physical states.

Graph construction captures cross-chunk dependencies that independent ranking misses.

Corrections update a structured specification and selectively invalidate only affected computations.

The source-grounded framework records statements, assumptions and inference links with review status and a fixed measurement contract.

Hypotheses about an agent's beliefs are scored only against evidence that agent could access, not the full episode.

The work uses AI-found circuits to extract principles for new deterministic compilation algorithms.

Verified advancement serves both as execution state and as supervision for learning operations.

Judges should not count relays and paraphrases as independent evidence, nor merge away genuine corroboration.

A policy agent uses recorded outcomes to organize combinatorial-optimization search under budget.

The piece frames AI agents as working beneath, beside and between human employees and sketches how firm structure may change.

It pairs algorithm synthesis with Intel's spin-qubit chips.

Italy has said observer status does not yet imply an industrial role.

Using T1DiabetesGranada data from 736 people with type 1 diabetes, the study compares sequence, multimodal and state-updating digital-twin models at 30, 60 and 120 minute horizons with emphasis on reliability.

A model-free policy meets noise constraints with less power curtailment than derating turbines.

Trained on teacher-generated candidate lists, it constructs validated alternatives so a decision model can choose without sequential decoding.

The paper proves degree gating beats reweighting when attackers cannot afford to open the gate.

CPOG certificates from the d4 counter become verified counts of combinatorial objects.

Target-agnostic and target-specific computation are separated so switches take effect immediately.

Exploration is constrained so users never see two poor options.

Zoom is matched to target size and wrong answers are revisited.

swyx's tracker finds frontier models from Astra to Fable to Muse almost always pick Supabase for app backends, and the founders discuss what makes generated apps actually run.

The devices deepen in-home personalization and data collection.

QUAN decomposes the Hamilton product into real-valued operations with custom CUDA kernels, adds an independent quaternion batch norm, and applies the approach to image classification and oriented object detection.

The twin replays learning traces and assessment records at temporal checkpoints, serving both as a feature substrate for risk prediction and as an explanation layer linking alerts to specific evidence.

A single amortized model estimates transfer entropy at each lag and tracks how delays shift over time, replacing separate fits across source and conditioning depths.

The method enables multi-layer GNN computation across separate bank graphs without account-level exchange, then prices whether sharing the remaining context is worth its disclosure.

The benchmark tests whether models separate near-neighbor syndrome labels, update as evidence fields are revealed, and decide when to commit under uncertainty.

Following an MBFC-style method with inspectable intermediate evidence, SCOPE beats LLM-only and search-only baselines on most metrics over 200 outlets.

Rather than learning item relations implicitly via next-token prediction, REGER injects semantic and collaborative structure into SID embeddings.

Planner, specialist and aggregator agents aim to reduce the prompt sensitivity of single-LLM judges at platform scale.

The method targets retrieval of plausible evidence from the wrong period, which can make independent event checks validate a false chronology.

Running SPARQL over source-grounded RDF records exactly which extracted statements produced each answer.

It controls Fisher sensitivity over a neighborhood of future updates rather than only protecting current parameters.

Synthetic graph-structured priors let one model forecast on new sensor networks without retraining.

Process-oriented modelling identifies incongruences such as unsatisfiable expectations or privilege escalation in agentic system designs.

Standard uncertainty scores misrepresent ambiguity for minority outcomes; corrections are proposed.

OpenAI's customer story describes contract analysis and legal workflow acceleration.

Local deployment makes archives searchable without sending material off-site.

At Steam Indie Connect, Valve said continuously added features and personality are harder to copy than a single mechanic.

It targets transcription bottlenecks in courts with large backlogs.

It avoids moving huge files over degraded networks.

The boundary aims to stop an erroneous identity hypothesis from producing self-confirming graph mutations.

Log-space sparse rate laws and implicit stiff solvers make atmospheric chemistry usable inside ML training loops.

Vertebral matching aligns unpaired slices to train a CT generator that could avoid extra radiation for surgical planning.

LLMs review statistically flagged cases to capture context-dependent anomalies at lower cost.

Expert-derived knowledge aims to catch defects off-the-shelf models miss.

Instruction edits make an original positive passage non-compliant, yielding scarce instruction-conditioned contrastive pairs.

It avoids assuming a single data subject when labeling which mentions to mask.

A comparative study tests guidance-only assistance versus test generation for learning.

It reconciles decisions, orders, ledgers and reports that can disagree even when final values are right.

A self-keyed scheme memorizes conversation content without external storage.

It corrects Gaussian scale inflation and semantic mixing.

Across 600 soundscapes and three models, different-class replacements are compared to matched same-class controls.

Larger planar hexagons with first-order color fields reduce primitive count.

Goldman Sachs' Kim Posnett says capital favors firms that train models and control chips and data centers.

It synthesizes grounded summaries scored by engagement.

DenseNet121 reached 99.4% test accuracy and 0.999 AUC on a patient-level split, outperforming transformer models, with Grad-CAM, LIME, Integrated Gradients and LRP used to explain predictions.

It jointly models sleep architecture and respiratory events and addresses embedding inconsistency for unseen patients.

An embedding model on the LILY ocean drilling database performs as well without expert labels.

The column argues leadership habits may mislead product design for mainstream audiences.

The venture targets demand for human input in AI products.

He covers persistent memory, private context and deterministic tools in Nautilo.

It is negotiating AI-voiced experiences with a carrier.

It adapts difficulty and works on low connectivity.

Generated pages are self-contained HTML and CSS.

It also adds Hot Exit.

It covers stacking and instrumenting agent loops.

The author argues payouts handle uncertainty about which jobs go when.

The project pairs a paper with an open implementation.

A former employee recalls the company's early culture.

It spans more than 80 providers.

The model needs neither the leak count in advance nor enumeration of leak combinations, tested on two benchmark networks.

The nonparametric CPDs reduce inference cost and density estimation error relative to one-component-per-observation KDEs.

The decomposition targets stability in sparse graphs.

It mitigates forgetting without external buffers.

A three-signal model combines session, 90-day history and user-graph encoders.

The two early program-writing systems are revived as theories of how people program.

It flags tumor types absent from reference cohorts.

Per-subject ablation attributions for cognitive prediction are less stable than often assumed.

Transcript counts show the term never outpaced AI.

A wiki compiler plus evidence graph preserves reusable knowledge.

Sequential and parallel composition of decompositions are used as atomic reasoning actions.

DDAR follows paths taken by similar individuals in data.

Polarity and timing branches feed geometric recovery and trajectory optimization.

Matching reliability controls context selection and update strength in weakly textured regions.

PsyJudgeBench gathers 13 datasets and 82 questions across six psychological areas under one question-conditioned interface.

Variants become temporal-logic specifications solved by generic tools.

Full collapse over-contracts tail classes; partial collapse generalizes better.

On-device classification serves as compression for untethered recordings.

Linear maps between frozen networks separate weak representations from weak readouts.

Learning from evaluated strategies avoids separate searches per target compound.

The sponsored poll finds 40% see moderate task-level gains.

Machine Learning Street Talk clip.

He cites translation as a white-collar job that has largely disappeared.

The feature mirrors X's Grok replies with neutral, thread-context invocation.

Research found deaf couriers earned 10% less per hour.

The $1 million program spans five countries.

The agent proposes ad changes from campaign data.

Scheduled runs support memory and triage.

It gives scanpy users a shareable viewer without converting to Seurat.

Augmentation, CutMix, MixUp and synthetic images address scarce and imbalanced plant-disease data.

It covers circular and axial means and distributions with wind, diffusion-MRI and ML examples.

Channel attention and saliency interpretability target dermoscopic lesion classification.

Time-to-arrival is estimated as the lower envelope of observed locomotion times.

Retrieval and agents aim to cut a two to three hour daily manual task.

It unifies fragmented process, property and product knowledge.

Regularization helps normalizing flows cover separated posterior modes.

It encodes elapsed time and band separation for cross-band CSI forecasting.

It models cross-regional coordination in Chinese support policies.

It extends LXR to more datasets and recommenders.

The paper treats atoms as agents predicting one another.

Statistics-aware prototypes handle dissimilar neighbors.

Pixel embeddings matched to landmark prototypes beat heatmap regression.

It switches between decomposition and non-decomposition algorithms.

Predicting latent fields avoids phase-sensitive raw CSI losses.

SUMO simulations compare deadline shortening and dedicated vehicles under three demand scenarios.

It balances booking conversion against host cancellation risk.

A 147-entity panel cuts forecast errors by over 39% versus linear models.

A one-million-row dataset is used to compare models.