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Beyond Saying Less: Fine-Grained Alignment for Informative and Faithful Vision-Language Models

arXiv:2609.35294v1 Announce Type: new Abstract: Object hallucination remains a major challenge for large vision-language models. While off-policy preference optimization proves to be an effective solu

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Beyond Site Agreement: Re-estimation for Brain Network Generalization

DGX agent

arXiv:2609.34611v1 Announce Type: new Abstract: Cross-site out-of-distribution (OOD) generalization in resting-state functional magnetic resonance imaging (rs-fMRI) often relies on learning task-discr

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29 Sept 2026

Beyond Teacher Assignment: Domain-Normalized Multi-Teacher On-Policy Distillation

arXiv:2609.35347v1 Announce Type: new Abstract: Reinforcement learning can turn one language model into several specialists, each excellent at a single skill such as mathematics, coding or following i

Beyond Timestamps: Decision-Aligned On-Policy Distillation for Long-Horizon Agents

arXiv:2609.33391v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) often relies on sparse outcome rewards, providing coarse supervision for long-horizon agents. On-p

BiasReducer: Adaptive Bias Mitigation for Reward Models

arXiv:2609.32720v1 Announce Type: new Abstract: Reward models score responses from large language models (LLMs) and guide LLM training toward human preferences. However, reward models can favor superf

Black-Box Auditing of Epistemic Reliability in Multi-Agent Debate Distillation

arXiv:2609.32361v1 Announce Type: new Abstract: Debate distillation adapts weaker verifiers using multi-agent debate transcripts to improve their judgement in subsequent debates, but gains on monitore

Brain-Conditioned Action Policies for Neural Motor Decoding

arXiv:2609.34561v1 Announce Type: new Abstract: Motor brain-computer interfaces (BCIs) aim to decode motor intention, enabling people with paralysis to control external devices. Neural motor decoding

🚨BREAKING: “Months before OpenAI's artificial intelligence went rogue, two employees raised an alarm with top executives. They were ignored…

🚨BREAKING: “Months before OpenAI's artificial intelligence went rogue, two employees raised an alarm with top executives. They were ignored….OpenAI executives told the employees that the tests needed

BREAKING: OpenAI postpones 6.1 for safety reasons. If we can take OpenAI at face value, this is quite a decision—and also a sign that no mag…

BREAKING: OpenAI postpones 6.1 for safety reasons. If we can take OpenAI at face value, this is quite a decision—and also a sign that no magical solution is in immediate view. Per @sheeraf at NYT “GPT

Bridging Generative and Discriminative Noisy-Label Learning via Direction-Agnostic EM Formulation

arXiv:2308.01184v4 Announce Type: replace Abstract: Although noisy-label learning is often approached with discriminative methods for simplicity and speed, generative modeling offers a principled alte

Cache-Aware Conv3D Lowering Across Embedded World-Model Decoders

arXiv:2609.31938v1 Announce Type: new Abstract: Generative world models can provide visual rollouts for embodied planning, yet their feasibility on edge devices depends not only on the learned model b

Can Circuit Alignment Predict OOD Generalization?

arXiv:2609.31996v1 Announce Type: new Abstract: Can out-of-distribution (OOD) generalization be predicted from a trained model's weights alone, without any target-domain data? Existing representationa

CAPEX: Efficiently Distilling Foundation Model Behavior into Deployable Robot Policies through Experience-Adaptive Reasoning

arXiv:2609.33007v1 Announce Type: cross Abstract: Robot learning has largely relied on human-teleoperated demonstrations to acquire effective learnable behaviors. However, human-operated data collecti

CapField-OPD: Learning Continuous Capability Fields via Joint-Anchored Multi-Teacher On-Policy Distillation for Flow Models

arXiv:2609.34658v1 Announce Type: new Abstract: Reward-specialized post-training produces strong experts for flow-based generative models, while multi-teacher on-policy distillation (OPD) consolidates

CaptchaArena: A Large-Scale, Fine-Grained Dataset for Training Computer-Use Agents on Interactive CAPTCHAs

arXiv:2609.31957v1 Announce Type: new Abstract: Interactive CAPTCHAs remain challenging for computer-use agents, while existing datasets face trade-offs among type coverage, interaction fidelity, and

CARVE: Breaking Data Barriers in Chip Placement by Harnessing Reusable Expertise

arXiv:2609.33106v1 Announce Type: new Abstract: Pretrained macro-placement policies can reduce repeated optimization across circuits, but deployment often exposes them to unfamiliar designs when the o

Certified Multi-Source Integrity for Structured Agent Actions

arXiv:2609.34245v1 Announce Type: cross Abstract: LLM agents increasingly take privileged, often irreversible structured actions, such as paying an invoice. They assemble each action from action-criti

ChatGPT, how much should two companies that have collectively contributed to the economy less than $30B be worth?

ChatGPT, how much should two companies that have collectively contributed to the economy less than 30B be worth? @Lio_Cicolecchia @GaryMarcus @ey Try the following in google or ChatGPT: 'how much audi

ChemOPD: Multi-Teacher On-Policy Distillation for Multi-Task Chemical Reasoning

arXiv:2609.33838v1 Announce Type: new Abstract: Large language models are increasingly expected to support diverse chemical reasoning capabilities within a unified model. One approach is to develop sp

CLAP: Closed-Loop Alignment with Pressure for Precise Suction Manipulation

arXiv:2609.32767v1 Announce Type: new Abstract: Stacking and palletising demand precise placement: error left in one layer is inherited by the next, and a flat pad offers no feature to funnel a wrong

Clarify the User or Verify the World? Uncertainty Routing for Proactive Agents

arXiv:2609.32255v1 Announce Type: cross Abstract: Tool-using LLM agents must decide not only whether additional information is needed, but also which source can resolve the uncertainty. Existing proac

Clinical Trajectory Alignment for Medical Vision-Language Pre-training

arXiv:2609.34439v1 Announce Type: new Abstract: Medical vision-language pre-training largely follows a visit-level image-report matching paradigm, aligning paired images and reports at individual visi

Clipped or Unclipped? Finite-Sample Trade-offs for Averaged SGD under Heavy-Tailed Noise

arXiv:2609.32962v1 Announce Type: new Abstract: Gradient clipping is widely used to stabilize training, but it need not improve the statistical accuracy of averaged SGD, even under heavy-tailed noise.

CoBrush: A Hierarchical Planning Framework for Human-Robot Co-Painting

arXiv:2609.35476v1 Announce Type: new Abstract: Embodied co-painting requires a robot to repeatedly update a shared physical canvas while human intent evolves over interaction. Existing reference-driv

Coding Agent Memory Post-training: Unlocking the Memory Potential of Pre-trained File Operations for Long-Horizon Tasks via Reinforcement Learning

arXiv:2609.34422v1 Announce Type: new Abstract: Language-model agents increasingly tackle long-horizon tasks whose interaction histories exceed the model's active context. Recent work has begun to use

COGNIT-Guard: Calibrated Standalone Direct-Decision Guardrails with Heterogeneous CPU-NPU Confidence Cascading under Explicit Latency and False-Positive Constraints

arXiv:2609.33671v1 Announce Type: cross Abstract: When must a foundation-model safety gateway generate tokens, and when should it directly output a calibrated decision? We study calibrated standalone

CompassPlay: Rewarding the Proposer for Where It Moves the Solver

arXiv:2609.32228v1 Announce Type: new Abstract: In self-play, a proposer generates verifiable tasks to train a solver. Proposer rewards often depend on the solver's success rate, but equally difficult

CompliantWBC: Whole-Body Compliance for Heavy Humanoids via Force Latent Estimation and Residual Impedance Targets

arXiv:2609.33310v1 Announce Type: new Abstract: Whole-body compliant control is essential for deploying heavy humanoids under high payload in human-centric environments. Most prior force-aware learnin

ConCAD: Constraint-Aware Image-to-CAD Generation with Dual-Granularity Rewards

arXiv:2609.34494v1 Announce Type: new Abstract: Image-to-CAD generation seeks executable parametric programs that recover both the geometry and design intent of a reference object. Existing systems ar

Concept-Guided Fine-Tuning: Steering ViTs away from Spurious Correlations to Improve Robustness

arXiv:2603.08309v3 Announce Type: replace Abstract: Vision Transformers (ViTs) often degrade under distribution shifts because they rely on spurious correlations, such as background cues, rather than

Conditioned Direct Feedback Alignment via Activity and Error Geometry

arXiv:2607.18574v2 Announce Type: replace Abstract: Direct feedback alignment (DFA) trains hidden layers with fixed random projections of the output error. Even when this feedback provides useful cred

Constrained Edit Fields for Training-Free Flow Editing

arXiv:2609.33735v1 Announce Type: new Abstract: Text-guided image editing aims to perform a desired edit while preserving source content unrelated to it. Pretrained rectified-flow models enable traini

Constrained Flow Policy Updates: A Generalized Schrodinger Bridge View

arXiv:2609.32952v1 Announce Type: new Abstract: Online safe reinforcement learning (RL) seeks policies that maximize reward while satisfying safety constraints. Reward and safety can induce multimodal

Contact-Aware Impedance Controller for Robot-Assisted Ultrasound Imaging

arXiv:2609.34400v1 Announce Type: new Abstract: Safe robot-assisted ultrasound imaging requires a reliable controller able to detect and localize probe--tissue interaction. In this paper, we present a

Contextual Distributionally Robust Optimization with Causal and Continuous Structure

arXiv:2601.11016v3 Announce Type: replace-cross Abstract: We propose a framework for contextual distributionally robust optimization (DRO) that considers the causal and continuous structure of the und

Convex Optimization Is Free When Accuracy Is Expensive

arXiv:2609.35418v1 Announce Type: cross Abstract: This paper studies convex optimization when the gradient cannot be evaluated exactly, but only approximated by a hierarchy of algorithms whose compute

Correcting the Dropout-LayerNorm Expectation Gap Improves Protein Structure Models

arXiv:2609.32062v1 Announce Type: cross Abstract: Although E[dropout(x)] = x, here we show that E[LayerNorm(dropout(x))] is not equal to LayerNorm(x). Accordingly, the pattern of a Dropout layer follo

CORTEX: A Verified Experience Layer for Generalist Agents

arXiv:2609.33260v1 Announce Type: cross Abstract: An agent can solve a task today and face the same task under new facts, tools, or governing knowledge tomorrow. Most agent systems can retrieve releva

could anything prove more decisively that Jensen Huang is a poor judge of character? his trust in OpenAI is entirely misplaced.

could anything prove more decisively that Jensen Huang is a poor judge of character? his trust in OpenAI is entirely misplaced. 🚨BREAKING: “Months before OpenAI's artificial intelligence went rogue, t

Counterfactual Attention Policy Distillation for Temporal Video Grounding

arXiv:2609.34581v1 Announce Type: new Abstract: Temporal video grounding is a key capability of advanced Multimodal Large Language Models (MLLMs) for the thorough understanding of video events, which

Counterfactual Rollout Replay: Forkable Environments as Free Process Rewards for Software Engineering Agents

arXiv:2609.33875v1 Announce Type: cross Abstract: Outcome-only reinforcement learning gives software engineering (SWE) agents a terminal success signal but little direct guidance about intermediate de

CTP-FL: Common-Trajectory Gradient Prediction for Federated Learning

arXiv:2609.35130v1 Announce Type: new Abstract: Communication-efficient federated optimization commonly spends several gradient evaluations between server updates. Existing local-update methods use th

De-biasing Skeleton-based Action Recognition with Convex Hull Adaptive Shift

arXiv:2609.32454v1 Announce Type: new Abstract: Skeleton sequences can represent both individual actions and multi-entity interactions, encompassing human bodies, hands, objects, and robots. Existing

Deep kernel hedging

arXiv:2609.34474v1 Announce Type: new Abstract: We introduce a deep kernel hedging framework that combines the flexibility of deep learning with the structural inductive bias of kernel methods. The he

Deep Weighted Bellman Residual Minimization for Q^* Estimation

arXiv:2609.34593v1 Announce Type: new Abstract: Off-policy evaluation is a foundational component of offline reinforcement learning, aiming to assess and optimize policy performance using pre-collecte

DeltaWAM: Change-Centric Visual Foresight via Delta Tokens for an Efficient World-Action Model

arXiv:2609.33177v1 Announce Type: cross Abstract: World-Action Models (WAMs) offer visual foresight for robotic manipulation, but pixel-space models repeatedly reconstruct entire future scenes, incurr

Demonstration-Free Success-Probability Reward Learning for Generalist Robot Policies

arXiv:2609.33653v1 Announce Type: cross Abstract: Reinforcement learning (RL) enables generalist robot policies to improve through trial-and-error interaction, yet its effectiveness is fundamentally c

Depot-Closed Multi-Component Construction for Neural Vehicle Routing

arXiv:2609.35066v1 Announce Type: new Abstract: Most neural constructive solvers for the vehicle routing problem (VRP) use route-by-route construction, extending one route until completion before star