ZeroHour
DashboardNewsCVEsExploitsIncidentsAIBriefAnalyticsEntitiesWatchlistIOCsAsk the feedSourcesFeeds & APIKiosk mode

Search: “verifiers”

112 stories

Searchkeywordsmart Sortlatesthotimportancerelevance
DomainAllCyberAIWindow24h3d7d30dAllIn the wildImportant onlyShow duplicates
CategoryAllVulnerability 1337Malware 967Phishing & fraud 908Data breach 838Ransomware 703Exploit / PoC 556Industry 547Policy & legal 528Threat actor 399AI safety & security 153Research 146AI research 113AI tools & infra 99AI industry 87
SeverityAllcritical 2339high 1829medium 1521low 1265info 580

GPT-5.6 Luna vs. GPT-6 Astra: Is a $1.20 Model Good Enough for Code Review?

Entelligence benchmarks GPT-5.6 Luna ($1.20/M output) against GPT-6 Astra for code review: Luna found 69 verified bugs at 3.6% of Astra's cost.

Entelligence compared GPT-5.6 Luna ($0.20/$1.20 per million tokens) against GPT-6 Astra ($10/$50) on 50 benchmark pull requests from Cal.com, Sentry, Discourse, Keycloak, and Grafana. Astra verified 92 bugs versus Luna's 69, with precision of 96% versus 74%, and Astra caught 19 of 24 security bugs while Luna found only 9. Luna cost $0.20 total versus Astra's $5.66 and reviewed faster at 23 seconds versus 36, with the widest quality gap on Keycloak authentication and permission logic (6 vs 14 verified bugs). Running both models would find 82% of the 143 verified bugs for $5.86 total.

Hacker News · AI · 2d agoAI researchHN 44↑ · 45 comments

SWE-Bench Pro Verified: A Reliable Benchmark for Software Engineering Agents

SWE-Bench Pro Verified is a corrected benchmark showing prior coding-agent scores were inflated by reward hacking and flawed tasks.

Analysis of SWE-Bench Pro found its evaluation undermined by reward hacking from leakage of gold solutions or hidden evaluation information, plus task quality issues such as misleading problem statements and improperly scoped tests. The authors present SWE-Bench Pro Verified, combining anti-hacking safeguards that eliminate major leakage channels with minimal task refinements. Evaluations show some models perform substantially worse than previously reported, suggesting SWE-Bench Pro overestimates real software engineering capability.

Hugging Face daily papers · 9d agoAI research1

FlowBalance: Verifier-Grounded Self-Improvement from On-Policy Reasoning Experience

FlowBalance is a verifier-grounded self-improvement method that beats FlowRL on Qwen3-4B and Qwen3-8B math reasoning while improving training stability.

FlowBalance calibrates dense self-guidance scores with verifier-derived group advantages: guidance is retained on positive-advantage trajectories, reversed on negative-advantage trajectories, and disabled when rollout groups show no outcome preference. The method exponentially reweights a reference policy via trajectory balance, with guarantees including within-group contrast preservation and a minimum-change reverse-KL characterization. On mathematical reasoning it outperforms FlowRL on Qwen3-4B and Qwen3-8B, trains faster and more stably, avoids direct OPSD's response-length collapse, and shows higher correct-strategy diversity on AIME24.

Hugging Face daily papers · 14d agoAI research

Verify Before You Distill: Prompt-Level Teacher Gating for On-Policy Distillation

TGOPD verifies teacher reliability per prompt before on-policy distillation, outperforming vanilla OPD across math, code, and instruction benchmarks.

Teacher-Gated On-Policy Distillation (TGOPD) estimates teacher reliability from verifier-scored teacher probes and routes each prompt either to dense on-policy distillation or to verifier-grounded GRPO, avoiding misleading updates from confidently wrong teachers under mode-seeking reverse KL. Across 4B and 35B students in mathematics, code, and instruction following, TGOPD outperforms vanilla OPD in all six single-domain settings and achieves higher seven-benchmark averages under multi-domain training. It also raises teacher-node GPU utilization from 9.8% to 78.9% in the measured 4B single-domain run by reusing idle teacher capacity.

Hugging Face daily papers · 15d agoAI research

Evaluating Verified Autonomy in Quantum Engineering

Quantum-Harbor lab and QIQCBench (49 tasks) expose wide performance gaps across 17 frontier agentic systems in verified quantum engineering.

Researchers built Quantum-Harbor, a virtual laboratory providing a controlled execution environment where scientific AI agents interacting with quantum systems can have both actions and conclusions directly verified. QIQCBench contributes 49 expert-authored tasks spanning calibration and control, error correction and compilation, and sensing and networking. Across 17 frontier agentic systems, verified performance varied widely, exposing a substantial gap between demonstrated capability and reliable autonomous operation.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research1

Verifiable by Construction: Claim-Level Evaluation of Verbatim Citation in Clinical Question Answering

Evaluation of twelve LLMs on 222 clinical questions shows verbatim quotes rarely substantiate claims; claude-opus-5 fully substantiates only 37.1%.

The authors build a standardized harness over four clinical practice guidelines and evaluate twelve LLMs on 222 synthetic clinical questions, measuring citation attachment, verbatim quote production, and claim substantiation. Most models attach verbatim quotes to over 90% of claims from prompting alone, though lightweight models like claude-haiku-4.5 struggle. Quotes frequently fail to substantiate claims: claude-opus-5 quotes 98.0% of claims but fully substantiates only 37.1%, exposing a capability gap for verifiable clinical QA.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research

Verifiable Social Reasoning for LLM Assistants

Fuse, a multi-agent simulation with hidden motives, evaluates LLM social reasoning, revealing compounding difficulty from user mediation and bias sensitivity.

Fuse is a multi-agent simulation framework in which a target agent with a hidden motive interacts with other agents including one representing the user, who consults the evaluated assistant to infer the motive, providing verifiable ground truth by construction. Simulation faithfulness is validated through a human study with 24k annotations. Applied to 12 LLMs, it shows user mediation compounds social reasoning difficulty, models are systematically sensitive to biased user framing, models may need more details than humans, and longer conversations do not always improve performance. The framework and a 21k-example dataset are open-sourced.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research

ProgramDistill: From Interactive Web Apps to Verifiable Reference-Guided SWE Tasks

ProgramDistill is a benchmark evaluating coding agents on reconstructing web app features from reference applications, testing nine frontier agents.

ProgramDistill evaluates coding agents on features discovered through interaction with fully functional reference applications, factorizing apps into features with replayable behaviors verified via gold patches. Its mine-craft-patch pipeline discovered 1,975 replay-verified behaviors across 26 applications and built 4,063 tasks without human intervention. On cumulative full-application reconstruction workflows, GPT-6 Astra achieved 49.2% and Claude Opus 5 28.8% success. In partial reconstruction, success drops from 100% to 64.0% and from 96% to 32% as restoration depth increases from 1 to 8.

Hugging Face daily papers · 1d agoAI research

Google Research Releases ToolGrad: Answer-First Framework Hits 99.8% Pass Rate for Tool-Use Data Generation

Google Research and partners introduce ToolGrad, a verified tool-chain-first data generation framework reaching 99.8% pass rate and boosting Gemma-3-12B to 83.1 on BFCL.

Researchers from Google, the University of Tokyo, RIKEN AIP, and Tohoku University released ToolGrad, which inverts query-first tool-use data generation by executing and verifying API chains before annotating them with user queries. On the ToolBench database of 16,000+ APIs, ToolGrad raised generation pass rate from 63.8% to 99.8% while increasing tool uses per sample from 2.1 to 3.4 and cutting tool-use steps from 34.3 to 20.0. Fine-tuning Gemma-3 at 1B, 4B, and 12B parameters on the 500-sample ToolGrad-500 dataset lifted ToolGrad-12B to 83.1 on the Berkeley Function Calling Leaderboard, near Gemini 2.5 Pro at 83.2 and ahead of GPT-5 at 74.4. Code is Apache-2.0, with the dataset, PyPI package, and models available on Hugging Face.

MarkTechPost · 6d agoAI research1

Eliciting Weak-to-Strong Generalization with On-Policy Reverse Distillation

OPRD distillation enables weak-to-strong generalization by amplifying verifier-supported policy updates, outperforming existing RL and distillation methods with fewer student updates.

On-Policy Reverse Distillation (OPRD) evaluates a weak teacher's policy shift relative to its reference policy on student rollouts and amplifies the verifier-supported component of the student's policy gradient. This rescaling preserves the stationary points of policy optimization while letting the student learn beyond the teacher's capacity ceiling. In successive model transfer and multi-teacher distillation, OPRD achieves higher performance with fewer student updates than existing RL and distillation approaches, and response-style analysis shows students remain closer to verifier-RL-trained models than to their weak teachers.

Hugging Face daily papers · 9d agoAI research

OPEN-1B: A Fully Auditable Training Run

Open-1B releases a 1B-parameter model with bitwise-reproducible training, letting independent auditors verify every step of the run on commodity hardware.

The paper introduces a 'fully auditable' tier of model transparency: every training operation is reproducible with bitwise certainty on heterogeneous commodity hardware by imposing definite ordering on GPU kernel reductions, data batch ordering, and collective communication. Because replaying a full run on one machine is infeasible, a collective verification scheme lets many independent auditors certify individual steps covering the whole run. The authors release Open-1B with its full pretraining dataset, every intermediate checkpoint, the training codebase, and an audit harness. This rules out undisclosed data, injected biases, or backdoors that proof-of-learning or proof-of-training-data techniques cannot exclude.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research

Real-SWE: Benchmarking AI models on private, real-world, enterprise codebases

Real-SWE benchmark tests coding agents on licensed private enterprise codebases; top model Fable 5.1 resolves only 38.8% of tasks.

Real-SWE is a new benchmark evaluating frontier AI coding agents on tasks drawn from private production codebases licensed from real companies, spanning billing, tax calculation, and cross-service migrations. Fable 5.1 with Claude Code leads at 38.8% resolution rate (pass@1 over eight runs), followed by GPT-6 Astra Codex CLI at 33.8% and Gemini 3.8 Flash Gemini CLI at 31.2%. Tasks use native harnesses and realistic tooling including Docker, Kubernetes, PostgreSQL, Redis, and Linear; median reference solutions edit 11 files versus 6 for DeepSWE and FrontierCode.

Hacker News · AI · 4d agoAI researchHN 45↑ · 31 comments1

ExecCritic: Learn to Test, Test to Improve for Coding Agents

ExecCritic separates test generation from patching for coding agents, lifting SWE-bench Verified resolution to 72.6%.

ExecCritic pairs a test-verify-revise scaffold with role-specific reinforcement learning: a Test agent writes repository-native tests and a Repair agent fixes code from execution feedback, both using Qwen-3.5-35B-A3B backbones. Post-trained Qwen agents compose to 72.6% on SWE-bench Verified, an 11.4-point gain over the 61.2% no-test baseline, without stronger-model or oracle feedback at evaluation time. The work shows test quality is the key variable: base-agent tests lowered resolution to 57.3% while GPT-5.6-sol tests raised it to 65.3%.

arXiv cs.AI / cs.LG / cs.CL · 8d agoAI research1

ThinkPrior: Zero-Rollout Difficulty Priors for Cold-Start Prompt Selection in RLVR

ThinkPrior builds zero-rollout difficulty priors via an offline verifier-anchored pass, halving silent groups in RLVR and cutting wasted rollouts on Qwen2.5-Math-7B.

In GRPO-based RLVR, groups where all rollouts are correct or all are wrong yield zero advantages and consume about 39% of a run's rollouts under uniform sampling. ThinkPrior initializes a Beta posterior from an external anchor pass's verifier-scored pass rate, selecting prompts by expected learnability before any target-policy rollout, without changing the loss or optimizer. On Qwen2.5-Math-7B across sixteen seeds it more than halves early silent groups and cuts wasted rollouts through step 30 by nearly a fifth, with no detected final-accuracy difference. The ThinkPrior+DAPO composition reduces generated rollouts by 10.6% at an equal 3,840-rollout update budget.

arXiv cs.AI / cs.LG / cs.CL · 8d agoAI research1

EmbodiedSkills: A Unified Framework for Orchestrating, Training, and Deploying VLA Agents

Researchers introduce EmbodiedSkills, a framework treating VLA skill decisions as verified execution proposals, reaching 86.2% success on RoboTwin 2.0.

The EmbodiedSkills framework treats each vision-language-action skill decision as an execution proposal, checking prerequisites before execution and verifying outcomes afterward via a shared executable-skill interface. It connects high-level skill selection, bounded low-level VLA execution and post-action verification in a single agent loop, and logs structured trajectories for supervision and optional online adaptation. Instantiated with Qwen3-VL and OpenPI/pi0.5, task-adapted policies achieve 86.20% average success across 50 RoboTwin 2.0 tasks and 97.40% across the four LIBERO suites, with 12.5% on memory-dependent RMBench tasks.

Hugging Face daily papers · 16d agoAI research1

Import AI 468: 23 RSI ideas; PostTrainBench+; and how trust and transparency interplay with AI racing

Import AI covers 23 IFP policy ideas for automated AI R&D risks and MIT/Columbia's game theory of AI racing slowdowns.

Think tank IFP published 23 policy recommendations across seven categories to help policymakers address risks from increasingly automated AI R&D. MIT and Columbia researchers released 'Racing to Ruin,' a game theory model showing that coordinated slowdowns between rival AI firms hinge on trust and transparency. The newsletter also links a short story on interacting with powerful AI systems.

Import AI · Aug 10, 2026AI research

Can Skills Learned in Games Transfer to Real-World Work?

Good Start Labs trains models in strategy games like 1830 and Diplomacy, showing terminal-agent training transfers to financial research benchmarks.

Good Start Labs, spun out of Every with $3.6M from General Catalyst and Inovia, trains AI models in verifiable strategy games. A 30B model trained as a multi-turn terminal agent in 1830: The Game of Railroads and Robber Barons improved Finance-Agent benchmark performance, while single-turn QA training did not transfer. The founders also co-authored COS-PLAY, a paper on co-evolving LLM decision and skill-bank agents for long-horizon tasks.

Latent Space · 1d agoAI research

ScienceIDE: Turning World's Scientific Codebase into Agent Learnable Environments

ScienceIDE converts scientific code repositories into verifiable agent training environments, producing the PhAI-IDE 4B-72B model family.

ScienceIDE turns scientific code repositories into executable environments supporting task generation, execution, and scientific verification, guided by expert-defined scientific cases and acceptance criteria. Using verified interaction trajectories, the authors train PhAI-IDE-72B, PhAI-IDE-9B, and PhAI-IDE-4B. The model family shows gains in held-out scientific-code repair and selected general-purpose code, reasoning, and knowledge benchmarks, evidencing positive transfer from scientific experience.

Hugging Face daily papersupdated · 13h agofirst · 1d agoAI research 2 sources

Unsolved Problem by Fields Medalist Breached by Two High School Students

Two high school students used Claude Opus 5 and GPT-5.6 Sol to help solve an open Lorentzian polynomials problem, posting a 75-page arXiv proof.

Aayush Bathija and Prince Rohatgi of Oak Park High School, mentored by UCLA postdoc Daniel Soskin, published the 75-page paper 'Bounded Ratios for Lorentzian Polynomials' (arXiv 2609.05341), solving an open problem in Fields Medalist June Huh's Lorentzian polynomial theory. The main structural theorem extends bounded coefficient-ratio characterization from quadratic to arbitrary-degree polynomials via discrete convexity conditions. The students used Claude Opus 5 and GPT-5.6 Sol for exploration and proof ideas but independently verified all arguments; the result follows an open letter from 25 Fields Medalists voicing concerns about AI's impact on mathematical rigor.

Hacker News · security · 2d agoAI researchHN 22↑ · 19 comments

Claude Fable 5.1 Solves the Cyphral Distich, a 370-year-old cipher

Claude Fable 5.1 solved Sir Thomas Urquhart's 370-year-old Cyphral Distich cipher, recovering a hidden royalist prayer for Charles II.

Vals AI reports that Claude Fable 5.1 solved the Cyphral Distich, a 64-number cryptogram from Sir Thomas Urquhart's Logopandecteision unsolved since 1653, in 44 minutes using 176k tokens with no human hints. The key insight was that the cipher's key was the book itself: each number indexes a word in the corresponding Proquiritation, taking the first letter, yielding 'O GOD UPHOLD KING CHARLS THE SECOND AND MAKE HIM THE SUPREME RULER OF THIS LAND'. The model also deciphered the larger Cyphral Octastich (285 numbers) from The Jewel (1652) using page-based word indexing, recovering all but nine letters of a royalist prayer. The puzzle had been listed among Klaus Schmeh's Top 50 unsolved encrypted messages.

Hacker News · AI · 3d agoAI researchHN 63↑ · 6 comments1· 1 read

ModularRSI: Modular and Generalizable Recursive Harness Self-Improvement

Researchers propose ModularRSI, a modular benchmark-disjoint recursive self-improvement framework that evolves agent harnesses across five modules, improving TB2.0 and SWE-Bench Verified results.

ModularRSI targets generalizable recursive self-improvement (RSI) for agent harnesses by contrasting successful and failed trajectories for the same task and aggregating evidence across tasks to find recurring behavioral deficiencies. It decomposes the evolvable harness into five modules—Agent Loop, Tool Use, Observation Management, Context Management, and Task Completion Detection—each evolved independently within a restricted scope, then integrated with conflict resolution. Using 2,000 executable evolution tasks disjoint from evaluation benchmarks, it shows consistent gains on TB2.0 and SWE-Bench Verified and transfers across different foundation models.

Hugging Face daily papers · 3d agoAI research

[AINews] OpenAI reports Navier-Stokes singularity find in 88 hours using Astra-next, roughly 10,000 agents and 130B tokens (>$40M), a contender for second ever Millennium Prize awarded

OpenAI-linked accounts claim roughly 10,000 AI agents produced a Navier-Stokes singularity result in 88 hours, pending mathematical verification.

OpenAI-affiliated accounts claim a system of roughly 10,000 agents, trained over about a year with multi-agent reinforcement learning, produced a finite-time singularity result related to the Navier-Stokes Millennium Problem. The claimed 88-hour runtime and 130B-token cost circulate only via social posts, and no preprint, theorem statement, or proof artifact is available. Acceptance by the mathematics community is unresolved, so the claim's epistemic status remains unknown. The roundup also notes Cognition's $48B and Mistral's $24B fundraises, GPT Image 2.5, and Meta's Muse agent relaunch.

Latent Space · 8d agoAI research1

Scores Alone Do Not Prove Discovery: The Discovery Certification Protocol for Auditing AI Research Agents

Researchers introduce the Discovery Certification Protocol, an auditable test framework that verifies whether AI research agents' claimed discoveries are genuine.

The Discovery Certification Protocol (DCP) converts AI research agents' discovery claims into executable recovery and feedback tests organized as gated audits. Controlled audits in SQLite optimization and virtual catalyst control produced zero recoveries in 96 episodes, with an upper bound of 0.0468. A deterministic, LLM-free verifier reproduces audit decisions from frozen evidence, giving AI research a common evidence language for outcomes, alternative routes, and feedback effects.

Hugging Face daily papers · 10d agoAI research

Stanford Researchers Release Paper2Agent: Turning Research Papers Into AI Agents That Reproduce Results and Run on New Data

Stanford researchers released Paper2Agent, a Nature-published pipeline that turns research papers into MCP servers agents can execute.

A Stanford team led by Jiacheng Miao and James Zou published Paper2Agent in Nature on 16 September 2026. Built on Claude Code's agent SDK, it converts a paper and its codebase into a Model Context Protocol server with validated tools, resources, and prompts. In benchmarks, the AlphaGenome agent built 22 tools in about 45 minutes for US$14, scored 100% on 15 novel queries versus 78.7% for Claude Code with repository access, and cut median runtime 1.9x. In scale tests, 74 of 100 bioRxiv papers were converted and 593 of 599 proposed tools passed validation.

MarkTechPost · 9h agoAI research1

Coding Agents Have Converged: Why the SWE-bench Leaderboard Can No Longer Order Its Top Entries, and What to Measure Instead

Audit of 254 SWE-bench submissions finds top coding-agent entries statistically inseparable, so small leaderboard gaps no longer establish rank.

The paper audits 254 SWE-bench submissions across four splits without running models. On Verified, the top two entries each resolve 396 of 500 instances, and exact paired McNemar tests separate none of the 29 adjacent top-thirty pairs at alpha=0.05. Within-model scaffold score ranges reach 29.8 percentage points, versus an 8.8-point spread among the top thirty. The authors release a five-step audit protocol and recommend reporting comparison-set-specific resolution and model-scaffold provenance.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research

Recurrent GraphNeural NetworkswithSet-BasedAggregation

Paper proves two-directional equivalence between recurrent GNNs with set-based aggregation and Boolean closure of reachability/safety properties in modal mu-calculus, checkable from weights.

The authors study recurrent graph neural networks with set-based aggregation and identify sufficient conditions, checkable directly from network weights, for compiling networks into logical formulas and formulas into networks. They establish an effective two-directional equivalence with the Boolean closure of reachability and safety properties, the fragment BΣ°1 of the modal μ-calculus, shown to be the exact expressive level of stabilization over finite vocabulary. The correspondence needs no counting logic, external halting signal, or non-effective acceptance condition, yielding a verifiable path from weights to symbolic explanations.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research

Omni-Streaming Thinking

Omni-Streaming Thinking fixes premature cross-modal commitment in streaming omni-modal models via pending claims verified against modality-specific evidence, beating baselines by over 10%.

The paper identifies 'premature cross-modal commitment', where streaming models keep relaying early visual interpretations even after audio contradicts them. OST generates evidence-linked pending claims with future verification intervals, stores audio and visual evidence separately, and refutes claims when contradictory evidence appears. Built on a frozen Qwen3-Omni-30B-A3B-Instruct backbone with lightweight adaptation, it outperforms open baselines by more than 10% relative on five streaming and audio-visual benchmarks. On the new OST-DiagBench it reaches d-prime 2.95 versus at most 1.38 for open baselines, while reducing vision-induced auditory hallucinations.

Hugging Face daily papers · 3d agoAI research1

RelateAnything: Real-Time Open-Vocabulary Relation Prediction From Any Inputs

RelateAnything is a 53M-parameter open-vocabulary relation prediction model running at 20 ms/frame, with 2.3-3.5x higher mean recall than comparable open-vocabulary methods.

RelateAnything predicts scored relations between image regions using any predicate vocabulary supplied at inference as text embeddings, with object labels never required as input, so region sources can change without retraining. Training covers 19,103 predicates using positive-unlabeled supervision; the authors release RA-4M (474k images, 4.3M geometrically verified relations over 10,102 free-text predicates) and the OV-SGG-Bench evaluation suite. The 53M-parameter model runs at 20 ms/frame and achieves 2.3-3.5x the mean recall of the strongest comparable open-vocabulary method across cross-dataset and zero-shot benchmarks. Model, corpus, and benchmark are public.

Hugging Face daily papers · 6d agoAI research1

T1: Terminal Agent Reinforcement Learning for Long-Horizon Tasks

T1, a 122B MoE terminal agent trained with reinforcement learning, reaches 64.0% on Terminal-Bench 2.1, surpassing GPT-5.4 and GLM-5.1 on long-horizon tasks.

T1 is a 122B mixture-of-experts model trained with reinforcement learning to operate a real shell in a cloud sandbox for up to 300+ tool-call turns per task, rewarded by executing each task's own verifier. The recipe combines aggressive warm starts, dense process rewards, TITO construction, and rollout routing replay, cutting the training-to-inference log-probability difference from 0.021 to 0.013 with zero token drift. Training used an out-of-distribution corpus disjoint from Terminal-Bench 2.1. Post-training raised the base model from 43.8% to 64.0% resolved on Terminal-Bench 2.1 and 27.9% on Long-Horizon Terminal Bench.

Hugging Face daily papers · 7d agoAI research1

Towards a Deterministic Math Solver for Clinical Language Models

Paper shows handing arithmetic to a deterministic Python solver beats direct model calculation at 32B but not reliably at 7B on MedCalc-Bench.

Researchers test a Program-Solve interface where clinical LLMs write case-specific Python executed by a restricted local solver instead of doing arithmetic directly. On MedCalc-Bench Verified (1,100 cases, 55 calculators), Qwen2.5-32B-AWQ scored 90.53% with solver handoff versus 83.47% with direct arithmetic (+7.05 points), while Qwen2.5-7B gained an unreliable +3.29 points with a confidence interval spanning zero. The authors audited the benchmark against clinical guidelines and flagged 16 of 55 calculators for version, use, or coefficient concerns.

Hugging Face daily papers · 8d agoAI research1

ReCite: Agentic Reasoning for Faithful Citation

ReCite is an agentic citation framework using claim-level reasoning and verification, outperforming large generative models in strict citation accuracy.

ReCite is a decoupled agentic framework that orchestrates location perception, intent-aware query planning, and reflective verification for citation recommendation. Trained on synthesized reasoning trajectories, the agent verifies claim-evidence consistency and triggers self-correction loops when retrieved candidates lack logical support. Experiments show the lightweight framework outperforms state-of-the-art massive generative models in strict citation accuracy, addressing misattribution where cited papers are real but logically unsupportive.

arXiv cs.AI / cs.LG / cs.CL · 8d agoAI research

Axis Robotics Releases AXIS: A Browser-Based Data Engine With 207 Robot Manipulation Tasks and 50,129 Trajectories

Axis Robotics and academic partners released AXIS, a browser-based teleoperation system yielding 207 manipulation tasks and 50,129 trajectories that lifts pi0.5 to 88.8 on LIBERO-Plus.

A team from Axis Robotics, UC Berkeley, Georgia Tech, and NTU introduced AXIS, a browser-based data engine where contributors teleoperate a simulated Franka Research 3 in a MuJoCo WebAssembly frontend while GPU backends handle task generation, training, and evaluation. The released snapshot holds 207 tasks, 50,129 episodes, and 60K+ task or scene variants from more than 70,000 community contributors. Continual pretraining of pi0.5 on AXIS data raises LIBERO-Plus performance from 83.9 to 88.8, versus 57.5 for a volume-matched RoboCasa365 control; the 2.36 TB dataset is gated for non-commercial academic use.

MarkTechPost · 9d agoAI research

CARDEA: Auditable Reasoning Grounded in Spatial Evidence for End-to-End Coronary Angiography Interpretation

CARDEA, a vision-language model trained only on public data, matches cardiologists on coronary angiography complexity assessment while exposing auditable bounding-box evidence.

CARDEA is a unified large vision-language model serving as the inference core of an end-to-end coronary angiography pipeline from multi-view videos to study-level diagnosis. It was trained on public datasets through visual alignment, self-distilled Chain-of-Box cold start, and reinforcement learning with verifiable rewards encouraging bounding-box reasoning. It reached 0.91 accuracy on dominance classification under domain shift and 0.90 on complexity assessment, comparable to two interventional cardiologists. RLVR raised zero-shot report generation vessel-severity macro-F1 from 0.513 to 0.686, while supervised imitation alone did not.

Hugging Face daily papers · 10d agoAI research