Search: “reasoning”

Advantage Scale Calibration Imbalance in Group-Relative Optimization under Low-Variance Rewards: Diagnosis and Bounded Recovery

arXiv:2609.19164v1 Announce Type: new Abstract: In verifier-style RLVR, group-relative optimization often treats advantage scale as an implementation detail. This paper separates two low-variance case

researcharxiv-cs-cl

Human

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Agents

AI Smart Glasses for Wearable Intelligence: From Egocentric Sensing to Agentic Personalization

DGX agent

arXiv:2609.19793v1 Announce Type: new Abstract: Recent advances in artificial intelligence (AI) are reshaping smart glasses from egocentric capture and display devices into platforms for wearable inte

agentsarxiv-cs-cv

18 Sept 2026

AntiGrounding: Executable Robot Trajectories as Visual Prompts for VLM-Guided Manipulation

arXiv:2506.12374v3 Announce Type: replace-cross Abstract: Natural-language manipulation instructions specify the task goal but leave the underlying robot trajectory unspecified. We present AntiGroundi

Architectural Design, Not Only Model Intelligence, Governs Multi-Agent LLM Performance

arXiv:2602.03128v2 Announce Type: replace Abstract: Multi-agent LLM frameworks are data-intensive systems that govern how agents orchestrate tasks, manage state, and coordinate decisions. These archit

Beyond the Foreground: FOV-Aware Polyp Image Synthesis via Lesion-Guided Adaptive Mucosal Context Propagation

model-releasesarxiv-cs-cv

arXiv:2609.19966v1 Announce Type: new Abstract: Synthetic image and mask pairs can alleviate scarce colonoscopy annotations, but realistic synthesis requires preserving the supplied lesion while gener

Coding Agents with an Obstacle-Aware Harness for Safe Robot Manipulation

arXiv:2609.20822v1 Announce Type: cross Abstract: Coding agents have emerged as a promising paradigm for robot manipulation: a language model writes the robot controller as a program, and agents built

Communication and Verification in LLM Agents towards Collaboration under Information Asymmetry

arXiv:2510.25595v2 Announce Type: replace-cross Abstract: While Large Language Model (LLM) agents are often approached from the angle of action planning/generation to accomplish a goal (e.g., given by

CoRef-GS: Cooperative Referring Gaussian Splatting for Multi-Agent Scene Understanding

model-releasesarxiv-cs-cv

arXiv:2609.20586v1 Announce Type: cross Abstract: Referring scene understanding for embodied robots requires grounding object- and relation-centric language queries from a designated viewpoint. While

DeltaSelect: Affordable A/B Testing for Coding Agents

arXiv:2609.19607v1 Announce Type: cross Abstract: Coding-agent benchmarks are built for broad and comprehensive comparisons, not frequent development decisions. Individual runs vary, full suites are e

Do AI Agents Understand Computer Architecture?

arXiv:2609.19387v1 Announce Type: new Abstract: Agents are increasingly asked to design hardware, and increasingly reported to succeed. Such reports establish that a design improved; they cannot estab

agentsarxiv-cs-ai

Effective and Efficient Threat Hunting with Small Language Models

arXiv:2512.06660v3 Announce Type: replace-cross Abstract: Analysts in Security Operations Centers query massive telemetry streams using Kusto Query Language (KQL), but writing correct KQL demands spec

Evaluating Out-of-Distribution Robustness in Graph-Based Android Malware Classification: A New Principled Benchmark

model-releasesarxiv-cs-lg

arXiv:2508.06734v3 Announce Type: replace-cross Abstract: While graph-based Android malware classifiers report strong benchmark accuracy of over 94%, their performance sharply decreases up to 45% when

FAMOS: Feed-Forward 3D Articulation Modeling from Sparse Observations

arXiv:2609.20817v1 Announce Type: cross Abstract: Modeling articulated objects from sparse monocular views is challenging because each observation reveals only partial geometry and motion evidence. Mo

From Rollout to Reset: A Graph-Based Harness for Autonomous Long-Horizon Manipulation Evaluation

arXiv:2609.19413v1 Announce Type: cross Abstract: Robot manipulation policies are improving quickly, and real-robot evaluation remains the standard evidence for that progress. It still relies on a hum

Full-Duplex Speech Models Take the Floor When Asked, Not When Needed

arXiv:2609.19596v1 Announce Type: new Abstract: Full-duplex speech models listen and speak at once, promising always-on assistants. Yet they must also decide when they should speak. Human listeners sp

researcharxiv-cs-cl

greCAPTCHA: Assessing Understanding as Evidence of Research Authorship Under Generative AI

arXiv:2609.20481v1 Announce Type: cross Abstract: Conferences, journals, funders, schools, and universities are struggling with a surge of potentially AI-generated submissions from ostensibly human au

How competitive are journals compared to top ai conferences? [D]

researchr-machinelearning

Hi everyone, The NeurIPS results aren’t out yet, but I’m expecting my paper to be rejected, so I’m considering submitting it to a journal instead. My scores were 2/3/3 (3/4/4). I feel like I would pro

I stopped using the smartest AI models. Programming got faster and cheaper.

Vacations give you time to think and explore. I came across a few things and made a few discoveries of my own. It turns out that, as a programmer, I no longer need the smartest and most expensive AI m

model-releasesr-ollama

It’s time for our end-of-week recap 👇 — Gemini 3.8 Live and 3.8 Live Extended Thinking, our most advanced live dialogue audio models yet — …

model-releasesgoogle-ai--x

It’s time for our end-of-week recap 👇 — Gemini 3.8 Live and 3.8 Live Extended Thinking, our most advanced live dialogue audio models yet — Dreambeans, an experiment from @GoogleLabs that curates a dai

Kinematics-Grounded Agentic AI for Robotic Additive Manufacturing Process Planning

arXiv:2609.19347v1 Announce Type: cross Abstract: Robotic additive manufacturing (AM) extends material-extrusion printing beyond gantry kinematics but makes process planning robot-dependent. A slicer-

agentsarxiv-cs-ai

Less Is More: Graph-free Multimodal RAG via Multi-signal Late Fusion

model-releasesarxiv-cs-cl

arXiv:2609.19417v1 Announce Type: new Abstract: Graph-based retrieval-augmented generation (RAG) is widely used for multimodal, cross-document question answering. However, building corpus-level graphs

LLM-as-an-Improver: Turning Verification into Better Candidates

arXiv:2609.19515v1 Announce Type: new Abstract: Verifier-based selection improves LLM performance by generating multiple candidate solutions and using a verifier to select the most promising one. Howe

MiniMax Code is now open source. Maybe M3.1 is the next thing to watch.

model-releasesr-localllama

MiniMax has released the source code for MiniMax Code’s terminal agent: https://github.com/MiniMax-AI/minimax-code https://preview.redd.it/st9izmm8zaqh1.png?width=3016&format=png&auto=webp&s=5853bc316

Navigate or Relocate? Planning Among Movable Obstacles in Unknown Environments

arXiv:2609.19541v1 Announce Type: new Abstract: Conventional robot planning methods seek collision-free paths to a goal but fail when all paths are blocked. In these cases, the robot must determine wh

local-aiarxiv-cs-ro

Applications

ParticleSplat: Self-supervised Object-centric Latent Particle Splatting

arXiv:2609.19463v1 Announce Type: new Abstract: We present ParticleSplat, a self-supervised object-centric learning method that decomposes scenes into a set of latent ''particles'' representing semant

applicationsarxiv-cs-cv

PersonalAI 2.0: Enhancing knowledge graph traversal/retrieval with planning mechanism for Personalized LLM Agents

model-releasesarxiv-cs-cl

arXiv:2605.13481v2 Announce Type: replace Abstract: We introduce PersonalAI 2.0 (PAI-2), a novel framework designed to enhance LLM-based systems through integration of external knowledge graphs (KGs).

Applications

Pretrained Medical Representations for the Practical Screening of Drug Repositioning Candidates

arXiv:2609.19865v1 Announce Type: new Abstract: Representation learning from medical code sequences in electronic health records and medical claims data has been successful in various clinical applica

applicationsarxiv-cs-lg

Prism-ML Bonsai 2 Joins Our Qwen3.8 Quantization Comparison

model-releasesr-localllama

Hey r/LocalLLaMA, Prism-LM recently released its Bonsai 2 QAT models based on Qwen3.8, and they quickly gained traction. In our evaluation, the models strike a strong balance between throughput and qu

QUATRO: Query-Adaptive Trust Region Policy Optimization for LLM Fine-tuning

arXiv:2602.04620v3 Announce Type: replace Abstract: GRPO-style reinforcement learning (RL)-based LLM fine-tuning algorithms have recently gained popularity. Relying on heuristic trust-region approxima

safetyarxiv-cs-lg

QVAC Genesis III: A Large-Scale, High-Quality Open Synthetic STEM Corpus for Efficient Language Model Pre-Training

arXiv:2609.19513v1 Announce Type: new Abstract: High-quality pre-training data is a critical bottleneck for educational and STEM-specific language models targeting edge AI and on-device deployment whe

Region-Level Policy Optimization for Fine-grained MLLM Perception

arXiv:2609.19745v1 Announce Type: new Abstract: Fine-grained visual perception in MLLMs is commonly improved by raising the resolution, but the added visual tokens inflate vision-encoding and language

local-aiarxiv-cs-cv

SemSafe-3DGS: Semantic Risk-Aware Active Navigation in Uncertain 3D Gaussian Splatting Maps

arXiv:2609.19330v1 Announce Type: new Abstract: Autonomous robots operating in partially observed environments must navigate safely while acquiring observations that improve future planning. Existing

safetyarxiv-cs-ro

SnapPhysics: A Physics-Aware Scene Graph from a Single View for Interactive Mixed Reality Scenes

model-releasesarxiv-cs-cv

arXiv:2609.19815v1 Announce Type: new Abstract: We propose SnapPhysics, a training-free framework that reconstructs 3D objects and estimates their physical properties such as mass, friction, and cente

SoL-Pi: Recursively Scaling Auto-Research Loops for Efficient Agent Harness

arXiv:2609.20519v1 Announce Type: new Abstract: As coding agents move from supervised code completion to unattended, around-the-clock exploration, their work expands from isolated predictions into lon

Super interesting work from Zoom and colleagues. If you maintain a hand-built coding harness, there are some great insights here. (bookmark …

Super interesting work from Zoom and colleagues. If you maintain a hand-built coding harness, there are some great insights here. (bookmark it) They held the execution loop of a coding harness fixed a

model-releasesdair-ai--x

The DevFest Community Workshop Experience: Building Real Agents Together

model-releasesgoogle-cloud-ai

This week we kicked off the DevFest season in North America at Google Hudson Square in New York City with 80 engineers packed into the room. Typical technical workshops hand you a finished repo, tell

Towards a Unified Modality-Agnostic Multimodal Framework for Cognitive Workload Assessment

arXiv:2609.20199v1 Announce Type: new Abstract: Cognitive workload reflects the mental effort required during task performance and is central to the design of adaptive human-machine systems. The use o

researcharxiv-cs-lg

VABench: Measuring Embodied Spatial Intelligence through Visual Demonstrations, Active Perception, and Metric Control

arXiv:2609.19554v1 Announce Type: cross Abstract: Spatial intelligence requires more than describing object locations. Under incomplete observation, models must identify and acquire missing evidence,

local-aiarxiv-cs-cv

VideoResearcher: Self-Improving Tool Design for Long-Video Understanding

arXiv:2609.19664v1 Announce Type: new Abstract: Video agents have made substantial progress in long-video understanding. Yet effective video-agent systems require costly, time-consuming manual design

agentsarxiv-cs-cv

VisKG-LM: Compiling Knowledge Graphs into Visual Memory for Multiple-Choice Question Answering

arXiv:2609.19158v1 Announce Type: new Abstract: Knowledge graphs are usually integrated into question answering by encoding a retrieved subgraph with a graph neural network and fusing it with the lang

local-aiarxiv-cs-cl

Workspace Models: Lightweight Robotic Memory via Saliency-Driven Supervision

arXiv:2609.20820v1 Announce Type: cross Abstract: Complex robotic manipulation tasks frequently require a long-term memory of past events and actions. As conditioning on full histories renders policie

Worst-Case Hidden-Vehicle Trajectory Search in Spatiotemporal Occlusion Regions

arXiv:2609.20480v1 Announce Type: new Abstract: Occlusion creates fundamental uncertainty in autonomous driving. Existing methods often propagate frame-wise hypotheses or optimize ego behavior against

agentsarxiv-cs-ro

A Comprehensive Review of Generative Physical Artificial Intelligence

arXiv:2609.18111v1 Announce Type: cross Abstract: The integration of large-scale foundation models with physical embodiments has led to significant advancements in robotics known as Generative Physica

Tutorials

ActiveScale: Scaling Active Perception for Robots across Model, Data, and Hardware

arXiv:2609.18514v1 Announce Type: cross Abstract: Active perception is essential for robotic manipulation when fixed viewpoints leave task-relevant information occluded or unobserved. However, enablin

tutorialsarxiv-cs-lg

ASPIRE: Asynchronous Batched Self-Speculative Decoding for Long-Context LLM Inference

arXiv:2609.17943v1 Announce Type: cross Abstract: Long-context LLM inference is bottlenecked by attention, whose repeated KV-cache reads make decoding memory-bound. Self-speculative decoding alleviate

researcharxiv-cs-cl

BadQubits: An LLM-Based Framework for Static Pre-Execution Detection of Structurally Harmful Quantum Circuits

arXiv:2609.18965v1 Announce Type: cross Abstract: This paper presents BadQubits, an LLM-based framework for static pre-execution detection of structurally harmful OpenQASM 2.0 circuits. The framework

Beyond Truncation: Rethinking LLM Decoding as Ensemble Pruning

arXiv:2609.18723v1 Announce Type: new Abstract: We introduce Mahalanobis-Ensemble Decoding (ME-Decoding), a novel Large Language Model (LLM) decoding framework that frames candidate token selection as

Bias Amplification in Multi-Agent Network: How Biased Agents Shape Opinions and Rhetoric

model-releasesarxiv-cs-lg

arXiv:2609.18306v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in applications involving interaction between agents, where their output plays a role in collecti