Search: “reasoning”

arXiv:2609.37539v1 Announce Type: new Abstract: Skills equip LLM agents with professional knowledge and guidance to complete long-horizon and complex tasks. Although skills have been widely adopted in

agentsarxiv-cs-ai

Local Ai

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SKILLLITE: Evidence-Guided Malicious Skill Auditing with Compact LLMs

DGX agent

arXiv:2609.36879v1 Announce Type: cross Abstract: As LLM-based agents perform increasingly complex tasks, Agent Skills have emerged as a flexible mechanism for extending their capabilities. An Agent S

local-aiarxiv-cs-ai

30 Sept 2026

SPLASH: Switching Parallel Layouts of Attention with Seamless Handoff for LLM Serving

model-releasesarxiv-cs-ai

arXiv:2609.37626v1 Announce Type: cross Abstract: No single way of parallelizing attention serves large language models well under all loads. Low concurrency favors tensor parallelism, many independen

STAR-GRPO: Canonical Anchoring and Reliability-First Advantages against Representation-Dependent Reward Hacking

arXiv:2609.36900v1 Announce Type: new Abstract: Reward hacking occurs when policy optimization exploits a brittle reward interface or an overly permissive proxy objective, improving the training score

T^2Mem: Learning Test-Time Memory for Robotics

model-releasesarxiv-cs-ro

arXiv:2609.36720v1 Announce Type: new Abstract: Memory-dependent robotic manipulation requires policies to use information that is no longer available in the current observation. Retaining history alo

TRACE: Deployable Tree-Relational Structure Enhancement for Oncology LLMs

model-releasesarxiv-cs-ai

arXiv:2609.35810v1 Announce Type: cross Abstract: Large language models are increasingly used in oncology applications, but their predictions are often weakly grounded in explicit medical structure. W

Train Ahead, Distill Back: Bootstrapping On-Policy Self-Distillation for Large Language Models

arXiv:2609.37132v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) improves large language models by letting a self-teacher with privileged information provide dense token-level superv

Training-Free Global Geometric Association for 4D LiDAR Panoptic Segmentation

arXiv:2512.18991v3 Announce Type: replace-cross Abstract: Dominant paradigms for 4D LiDAR panoptic segmentation are usually required to train deep neural networks with large superimposed point clouds

researcharxiv-cs-ai

UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG

model-releasesarxiv-cs-cl

arXiv:2603.28773v2 Announce Type: replace-cross Abstract: Large language models (LLMs) frequently generate confident yet factually incorrect content when used for language generation (a phenomenon oft

Using AFM 3 PCC as an alternative to Local LLM

Apple Foundation Model 3 through Private Cloud Compute is directly available in macOS 27.2 through familiar AI clients. For Mac users running local models such as Qwen 3.x or Gemma 4, AFM 3 PCC is a c

model-releasesr-ollama

V2X-WAM: A Cooperative World Action Model for End-to-End Autonomous Driving

arXiv:2609.37098v1 Announce Type: cross Abstract: Vehicle-infrastructure cooperation can complement onboard sensing with broader and more informative observations of the traffic environment, providing

VAA-CSEC: Vote-guided Advantage Allocation for Chinese Semantic Error Correction

arXiv:2609.36804v1 Announce Type: cross Abstract: Chinese Semantic Error Correction (CSEC) targets semantic errors in Chinese text, which are typically more subtle and complex than spelling and gramma

Visual sensitivity is not claim retractability: persistence-aware credit assignment for multimodal reinforcement learning

model-releasesarxiv-cs-ai

arXiv:2609.36572v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has been extended to Large Vision-Language Models (LVLMs), and perception-aware methods further en

Vulnerability Discovery and Exploitation Trends in the AI Era

model-releasesgoogle-cloud-ai

Written by: Robin Grunewald, Supriya Mazumdar, Kelli Vanderlee Introduction Google Threat Intelligence Group (GTIG) examines vulnerability disclosure and exploitation statistics to evaluate the impact

WayFinder: Hierarchical Visual-Language-Action for Zero-Shot Waypoint Generation and Low-Level Kinematic Control

arXiv:2609.37922v1 Announce Type: new Abstract: Visual Language Action (VLA) models offer unprecedented generalization for autonomous robots; however, their real-world deployment is frequently bottlen

safetyarxiv-cs-ro

Local Ai

Where Should Physics Enter a Molecular Crystal Generator?

arXiv:2609.36398v1 Announce Type: cross Abstract: Generative models make molecular crystal structure prediction fast, but their samples still exhibit geometric and packing violations. Physics can be i

local-aiarxiv-cs-ai

WUSH-KV: KV Cache Quantization with Data-Adaptive Transforms

arXiv:2609.38121v1 Announce Type: new Abstract: KV cache memory and bandwidth costs grow with context length and batch size, which limits efficient long-context inference. To address this bottleneck,

researcharxiv-cs-lg

You Cannot Pick a Provider From the Price List: Market-Aware Routing for Open-Weight LLM Inference

arXiv:2609.37902v1 Announce Type: new Abstract: Existing LLM routers choose among models using static per-model costs. We show that open-weight inference markets introduce a second, largely ignored de

400+ LLM agents living in a 2004-era MMO server, all local on Qwen3-4B

I wanted a fun way to visualize and stress-test a multi-agent ecosystem, so I pointed it at my favorite MMO (a vanilla-era private server emulator). Live view: https://design.kristiantalley.com/projec

model-releasesr-ollama

A Continuous-Time Reinforcement Learning Framework for Fine-Tuning Discrete Diffusion Models

arXiv:2607.14522v2 Announce Type: replace Abstract: We formulate reinforcement learning (RL) in continuous time with discrete state spaces and possibly arbitrary action spaces via a stochastic control

safetyarxiv-cs-lg

ActionLens: Diagnosing Spatial-Temporal Binding Failures in Vision-Language Models

arXiv:2609.34547v1 Announce Type: new Abstract: Video-capable vision-language models score above 80% on popular benchmarks yet struggle with spatial-temporal binding: associating the right action with

Adaptive Ensemble Selection for Noisy Labels on Tabular Data

arXiv:2609.32976v1 Announce Type: new Abstract: Incorrect or corrupted labels in tabular datasets can significantly degrade supervised learning performance, particularly when mislabeling is subtle and

Hardware

ADPTNet: Adaptive with Prescriptive Timescales Non-Linear SSM for Sequence Modelling

arXiv:2609.34034v1 Announce Type: cross Abstract: A central aim of neuromorphic computing is to provide a viable alternative to highly energy-intensive Transformer-based AI. However, efficient alterna

hardwarearxiv-cs-lg

Advancing Video-Text Pretraining with Multi-View Captions

arXiv:2609.35090v1 Announce Type: new Abstract: Video-text pretraining has achieved remarkable progress through the scaling of models and datasets, yet the quality of language supervision remains unde

researcharxiv-cs-cv

Advancing Wildlife Conservation through Multimodal Animal Re-Identification with Environmental Metadata

arXiv:2609.34094v1 Announce Type: new Abstract: Identifying individual animals is crucial for effective wildlife monitoring and conservation efforts. Recent advancements in computer vision have shown

researcharxiv-cs-cv

'Agent security will be solved in the open, collaboratively, together.' At Red Hat, open is the mission, so couldn't agree more. Red Hat has…

'Agent security will be solved in the open, collaboratively, together.' At Red Hat, open is the mission, so couldn't agree more. Red Hat has been part of the OpenShell project from the early days, as

safetyclem-delangue--x

AgentPerfBench: A Benchmarking and Evaluation Suite for Inference Performance of Agentic LLMs

arXiv:2609.34683v1 Announce Type: new Abstract: The optimization of LLM serving engines, such as vLLM and SGLang, is largely benchmark-driven: optimizations, scheduling policies, hardware and system d

Agents

AI-driven ionic liquid discovery with unified chemical intelligence

arXiv:2511.11257v2 Announce Type: replace-cross Abstract: The rational discovery of functional solvents remains challenging because macroscopic solvent behaviors emerge from complex many-body interact

agentsarxiv-cs-lg

An Empirical Study on What Matters for Viewpoint-Generalizable Policies in Visual Imitation Learning

arXiv:2609.32762v1 Announce Type: cross Abstract: Visual imitation learning is a promising approach to training robot manipulation policies capable of completing a wide variety of tasks. However, poli

An Imperfect Verifier is Good Enough: Learning with Noisy Rewards

arXiv:2604.07666v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is widely used for post-training Large Language Models, but practical verifiers can make error

safetyarxiv-cs-lg

AnesTRACE: Benchmarking Intraoperative Anesthesia from Multimodal Perception to Multi-step Decision-Making

arXiv:2609.32740v1 Announce Type: new Abstract: Intraoperative anesthesia requires systems to interpret evolving multimodal evidence, recommend timely management, and revise decisions as patient state

AquaBEV-Nav: Learned BEV Occupancy for Underwater Navigation and Exploration

arXiv:2609.32156v1 Announce Type: cross Abstract: Safe underwater exploration requires a robot to understand where surrounding structures are located and which regions are available for motion. Existi

researcharxiv-cs-cv

Audit-First VAPO: Risk-Certified Selective Updates under Imperfect Verification

arXiv:2609.33662v1 Announce Type: cross Abstract: Imperfect verifiers can assign a harmful update direction even when clipping and regularization bound its magnitude. We introduce Audit-First VAPO, wh

safetyarxiv-cs-lg

Auditing Information Disclosure During Large-Scale Gradient-Based Training via Gradient Uniqueness

arXiv:2510.10902v3 Announce Type: replace Abstract: Auditing information disclosure across every datapoint during the training of LLMs is challenging. We propose a principled, attack-agnostic approach

researcharxiv-cs-lg

Applications

Bayesian Active Learning for Intent Disambiguation in Interactive Robot Planning

arXiv:2609.34270v1 Announce Type: new Abstract: Interactive robot planning requires robots to infer and execute human intentions from natural language instructions that are often ambiguous, incomplete

applicationsarxiv-cs-ro

Beyond Retrieval Relevance: Scene-Grounded Risk Entailment for Vision-Language Driving

arXiv:2609.34145v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) gives vision--language driving systems access to external safety knowledge, yet a retrieved risk rule may be releva

safetyarxiv-cs-ro

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

CFLoRA: Federated Fine-tuning of LLMs with Complementary Factors for Error-free Aggregation

arXiv:2609.33147v1 Announce Type: new Abstract: Federated low-rank adaptation (LoRA) enables collaborative fine-tuning of large language models without centralizing private client data. Its factorized

CLIMB: A Clinical Multimorbidity Benchmark for Diagnosing Co-occurring Conditions through Multiturn Conversations

arXiv:2609.35462v1 Announce Type: new Abstract: Patients often have several co-occurring clinical conditions, and the findings needed to identify and disambiguate them emerge over the course of a cons

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

safetyarxiv-cs-ro

Composable Decoding on the Probability Simplex: Theory and Implementation

arXiv:2609.34992v1 Announce Type: new Abstract: Decoding for large language models is typically treated as a collection of isolated sampling strategies, with limited theoretical understanding of the b

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

ConvCue: Complementary Visual Inductive Biases for Vision-Language Models

arXiv:2609.34196v1 Announce Type: new Abstract: Modern vision-language models (VLMs) achieve strong performance across a broad range of multimodal tasks, yet still struggle with visual questions that

CT-OPD: Counterfactual Trace On-Policy Distillation for Diffusion Vision-Language Models

arXiv:2609.32781v1 Announce Type: new Abstract: Diffusion vision-language models generate answers by gradually resolving masked tokens, making accurate conditional prediction in partially resolved sta

Agents

CueKFS: Agentic Cue-Driven Keyframe Selection for Long Video Understanding

arXiv:2609.31873v1 Announce Type: new Abstract: Keyframe selection (KFS) has long produced compact video summaries for browsing and retrieval, and representative frames for thumbnails. More recently,

agentsarxiv-cs-cv

Curating Merchant-Matching Training Data with Two Confidence-Gated Local LLM Judges

arXiv:2609.33878v1 Announce Type: cross Abstract: Merchant matching resolves a noisy payment descriptor to a retrieved merchant entity or returns no match. A key challenge in curating training labels

Defending at machine speed: Securing the public sector in the agentic era

model-releasesgoogle-cloud-ai

Over the last three decades in cybersecurity, I’ve witnessed major paradigm shifts — yet none match the velocity and complexity of today’s landscape. Attackers are now using AI to move at machine spee

DeltaSeek: Toward Active Perception in Evolving Construction Environments

arXiv:2609.33836v1 Announce Type: cross Abstract: Construction environments evolve continuously, causing large geometric changes that degrade static mapping and registration performance. This necessit