Search: “reasoning”

Lilian Weng provides a comprehensive summary of 35 research papers focusing on Harness Engineering approaches for Reasoning and System Integration (RSI), covering recent developments in this specializ

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8 Jul 2026

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Research

Revisiting Scene Graph Generation from the Perspective of Detector-Conditioned Reachability

DGX agent

arXiv:2607.06176v1 Announce Type: new Abstract: Scene graph generation (SGG) approaches can be broadly classified into detector-based and query-based methods according to their underlying reasoning me

researcharxiv-cs-cv

8 Jul 2026

SCOReD: Student-Aware CoT Optimization for Recommendation Distillation

arXiv:2607.05734v1 Announce Type: cross Abstract: Chain-of-thought (CoT) distillation in the recommendation domain is a necessary precursor to RL training, but raw teacher traces are ill-suited to thi

8 Jul 2026

Static Metrics Are Insufficient: Predicting Java Method Energy Usage with Execution Time

arXiv:2607.06124v1 Announce Type: cross Abstract: The increasing energy demand of software systems is raising concerns about their environmental impact and associated costs. Reasoning on energy usage

8 Jul 2026

20 questions for the Agentic Enterprise (and how Agent Platform can help)

model-releasesgoogle-cloud-ai

If you’re an IT leader, you might be getting a lot of questions about how to build and deploy agents. The pressure to move fast is intense, but the engineering reality is incredibly complex. Where do

ACPO: Adaptive Credit Policy Optimization via Fine-Grained Surrogate Entropy

arXiv:2607.03126v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has substantially improved the reasoning ability of large language models (LLMs), but sparse outcome rewards still make to

AnchorVLA: Bridging Discrete Decisions and Continuous Trajectories for Vision-Language-Action Planning

arXiv:2607.03182v1 Announce Type: cross Abstract: Autonomous driving planning requires translating navigation intent, traffic rules, dynamic interactions, and language instructions into executable con

Applications

CARL: Constraint-Aware Reinforcement Learning for Planning with LLMs

arXiv:2607.04854v1 Announce Type: new Abstract: Despite their strong reasoning capabilities and extensive world knowledge, Large Language Models (LLMs) frequently generate plans that violate task cons

applicationsarxiv-cs-ai

CritiqueDriveVLM: From Verifier-Guided Reinforcement Learning to Latent Thought Distillation for Autonomous Driving

arXiv:2607.04179v1 Announce Type: cross Abstract: End-to-end Vision-Language Models (VLMs) show immense potential in autonomous driving. However, standard Supervised Fine-Tuning (SFT) often suffers fr

imo this is the most impt part of anthropic's J-space paper today. it's a two-parter: 1) ant proved that they can do 'brain surgery' interve…

imo this is the most impt part of anthropic's J-space paper today. it's a two-parter: 1) ant proved that they can do 'brain surgery' interventions into reasoning to change topics midstream* 2) THE MOD

model-releasesswyx--x

K9-Bench: Evaluating Multimodal LLMs on Canine-Centric Videos

arXiv:2607.02680v1 Announce Type: cross Abstract: MLLMs have shown strong zero-shot capabilities across diverse inputs such as across images, video, audio, and text. A crucial, yet underexplored, appl

Learning 3D Affordances for Blade Insertion in Cluttered Stowing

model-releasesarxiv-cs-cv

arXiv:2607.02549v1 Announce Type: new Abstract: Many manipulation tasks require reasoning about free-space affordances: discovering volumes where an extended rigid tool can safely navigate, complement

PixelPilot: Scalable Vision-Language-Action Models for End-to-End Autonomous Driving

arXiv:2607.04637v1 Announce Type: new Abstract: Vision-Language-Action Models (VLAs), which leverage the advanced reasoning capabilities of Vision-Language Models (VLMs), show promising generalization

safetyarxiv-cs-cv

Reading Between the Dots: Decoding Hidden Computation across Filler Tokens

arXiv:2607.03502v1 Announce Type: cross Abstract: Frontier LLMs can perform multi-step reasoning over content-free filler tokens like dots or counting sequences, producing correct answers with no visi

Re:Form -- Reducing Human Annotations in Scalable Formal Software Verification with RL in LLMs: A Preliminary Study on Dafny

arXiv:2507.16331v4 Announce Type: replace Abstract: Existing informal language-based (e.g., human language) Large Language Models (LLMs) trained with Reinforcement Learning (RL) face a significant cha

Rethinking On-Policy Self-Distillation for Thinking Models

arXiv:2607.05184v1 Announce Type: new Abstract: Self-distillation is a promising recipe for self-improvement in language models. In this setting, a model can serve as its own teacher when given privil

S-EMBER: A Large-Scale Benchmark for Streaming Egocentric Memory Retrieval

arXiv:2607.02689v1 Announce Type: cross Abstract: As wearable devices enable continuous first-person recording, AI assistants must reason across long time horizons to recall past experiences-a capabil

SpecEyes: Accelerating Agentic Multimodal LLMs via Speculative Perception and Planning

arXiv:2603.23483v2 Announce Type: replace-cross Abstract: Agentic multimodal large language models (MLLMs) (e.g., OpenAI o3 and Gemini Agentic Vision) achieve remarkable reasoning capabilities through

TREK: Distill to Explore, Reinforce to Refine

arXiv:2607.05339v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) is effective when the current policy already samples useful reasoning trajectories, but it stalls on hard pr

Unified Audio Intelligence Without Regressing on Text Intelligence

arXiv:2607.05196v1 Announce Type: cross Abstract: Audio intelligence involves understanding, reasoning about, and generating both audio and speech. In this work, we introduce Nemotron-Labs-Audex-30B-A

Unsupervised Features Mining via Activation Geometry

arXiv:2607.04222v1 Announce Type: new Abstract: Interpretability methods aim to reveal the features represented inside large language models (LLMs). Many existing methods begin with labeled examples o

McGovern neuroscientists have discovered that you don't need language to think logically. People with severe language impairments solve comp…

McGovern neuroscientists have discovered that you don't need language to think logically. People with severe language impairments solve complex reasoning puzzles perfectly and brain scans show languag

researchyann-lecun--x

6 Jul 2026

Adaptive Group-Based Counterfactual Explanations for Time-Series Rehabilitation Data

arXiv:2607.01838v1 Announce Type: new Abstract: Counterfactual explanations (CEs) for multivariate time-series classifiers are often difficult to interpret in domains where experts reason in terms of

researcharxiv-cs-lg

Bayesian Sparse Low-Rank Adaptation for Large Language Model Uncertainty Estimation

arXiv:2607.02182v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit remarkable reasoning capabilities, but their task-specific fine-tuning is notoriously plagued by overconfidence,

DemoPSD: Disagreement-Modulated Policy Self-Distillation

arXiv:2607.02502v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) has emerged as a practical method for training large language models (LLMs) to reason, where a single model acts as

Diverse Evidence, Better Forecasts: Multi-Agent Deliberation Under Information Asymmetry

arXiv:2607.01661v1 Announce Type: new Abstract: Multi-agent systems are increasingly used for forecasting future events, as deliberation among multiple LLMs is believed to improve reasoning and calibr

Fast Multi-dimensional Refusal Subspaces via RFM-AGOP

arXiv:2607.02396v1 Announce Type: new Abstract: Steering and monitoring activations in Large Language Models (LLMs) are increasingly used for both safety and interpretability. Early work assumed behav

LLMs as Teaching Assistants for Mathematics Exam Grading: Reliability, and Practical Usability

arXiv:2607.01247v1 Announce Type: cross Abstract: Open-ended mathematics exams are valuable because they assess reasoning, proof construction, algorithmic thinking, and communication of intermediate s

Tutorials

Visually Grounded Self-Reflection for Vision-Language Models via Reinforcement Learning

arXiv:2607.02490v1 Announce Type: new Abstract: Large vision-language models can reason over multimodal inputs by generating textual chains of thought (CoT). A key capability exhibited in CoT reasonin

tutorialsarxiv-cs-cl

Bayesian Uncertainty Propagation for Agentic RAG Pipelines: A Proof-of-Concept Study on Multi-Hop Question Answering

arXiv:2607.00972v1 Announce Type: new Abstract: Trustworthy deployment of Agentic Retrieval-Augmented Generation (RAG) systems requires mechanisms for estimating when multi-stage reasoning pipelines m

Even right now, many workflows are morphing into LRM-guided harnessess that manipulate symbolic programs. Which is a crude, but currently-ac…

researchfrancois-chollet--x

Francois Chollet discusses how current workflows are increasingly incorporating Large Reasoning Models (LRMs) as control systems that direct symbolic programs and tools, representing a pragmatic but i

From Prediction Uncertainty to Conformalized Distance Fields for Safe Motion Planning

arXiv:2607.00776v1 Announce Type: new Abstract: Safe motion planning in dynamic environments requires reasoning about the uncertainty in predicted obstacle motion without sacrificing real-time perform

safetyarxiv-cs-ro

Multi-scale Mixture of World Models for Embodied Agents in Evolving Environments

arXiv:2607.00457v1 Announce Type: new Abstract: Embodied agents operating in the real world require multi-scale reasoning and knowledge adaptation as conditions change. We identify two challenges in a

On Robustness and Chain-of-Thought Consistency of RL-Finetuned VLMs

researchapple-ml-research

Reinforcement learning (RL) finetuning has become a key technique for enhancing large language models (LLMs) on reasoning-intensive tasks, motivating its extension to vision language models (VLMs). Wh

Retrieved Images as Visual Thought: Training-Free Multimodal In-Context Learning for the Open-vs-Closed Gap

model-releasesarxiv-cs-cv

arXiv:2607.00606v1 Announce Type: new Abstract: Recent work on Thinking with Images makes vision a dynamic part of reasoning, but does so through generation: the model invokes external tools, synthesi

Local Ai

Where Am I? Semantic Map Grounding via Vision-Language Models for Multi-Modal Localization

arXiv:2607.01079v1 Announce Type: new Abstract: We address robot localization in GPS-denied indoor environments by reframing it as a semantic reasoning task rather than a geometric estimation problem.

local-aiarxiv-cs-ro

XSkill: Continual Learning from Experience and Skills in Multimodal Agents

arXiv:2603.12056v3 Announce Type: replace Abstract: Multimodal agents can now tackle complex reasoning tasks with diverse tools, yet they still suffer from inefficient tool use and inflexible orchestr

Beyond Static Prompts: Building Scale-Proof, Polymorphic Multi-Agent Systems with Google's ADK

model-releasesgoogle-cloud-ai

As enterprise generative AI transitions from simple, conversational chatbots to autonomous multi-agent workflows, developers face a critical bottleneck: scale. In a production environment, an enterpri

Google named a Leader in 2026 Gartner® Magic Quadrant™ for Analytics and Business Intelligence Platforms for third year in a row

model-releasesgoogle-cloud-ai

For the third consecutive year, Google has been recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Analytics and Business Intelligence Platforms. This recognition comes on the heels of Go

GR2 Technical Report

arXiv:2606.31984v1 Announce Type: cross Abstract: Industrial recommendation systems serve billions of users through a multi-stage funnel -- retrieval, early-stage ranking, and re-ranking -- where the

HealthAgentBench: A Unified Benchmark Suite of Realistic Agentic Healthcare Environments for Challenging Frontier AI Agents

arXiv:2606.31179v1 Announce Type: new Abstract: As AI agents become increasingly capable of complex, long-horizon reasoning, rigorous and holistic evaluation is essential for measuring progress toward

Local Ai

TAG-DLM: Diffusion Language Models for Text-Attributed Graph Learning

arXiv:2606.31166v1 Announce Type: new Abstract: Text-attributed graphs (TAGs), where each node carries a natural language description, require models to jointly reason over text and graph topology. Ex

local-aiarxiv-cs-cl

Teaching LLMs String Matching, Backtracking, and Error Recovery to Deduce Bases and Truth Tables for the Combinatorially Exploding Bit Manipulation Puzzles

arXiv:2606.23672v2 Announce Type: replace Abstract: This paper presents our algorithmic innovations for the NVIDIA Nemotron Model Reasoning Challenge, focusing on Bit Manipulation Puzzles. In this tas

The Bidirectional Process Reward Model

arXiv:2508.01682v3 Announce Type: replace Abstract: Process Reward Models (PRMs), which assign fine-grained scores to intermediate reasoning steps within a solution trajectory, have emerged as a promi

What If We Allocate Test-Time Compute Adaptively?

arXiv:2602.01070v5 Announce Type: replace Abstract: Test-time compute scaling allocates inference computation uniformly, uses fixed sampling strategies, and applies verification only for reranking. In

Can LLMs Prove Robotic Path Planning Optimality? A Benchmark for Research-Level Algorithm Verification

model-releasesarxiv-cs-ro

arXiv:2603.19464v2 Announce Type: replace Abstract: Robotic path planning problems are often NP-hard, and practical solutions typically rely on approximation algorithms with provable performance guara

30 Jun 2026

CLMASP: Coupling Large Language Models with Answer Set Programming for Robotic Task Planning

arXiv:2406.03367v2 Announce Type: replace Abstract: Large Language Models (LLMs) possess extensive foundational knowledge and moderate reasoning abilities, making them suitable for general task planni

30 Jun 2026

DRIFT: Difficulty Routing Self-DIstillation with Rhythm-Gated Exploration and Success BuFfer Training

arXiv:2606.30345v1 Announce Type: cross Abstract: Enabling large language models to achieve stable self-improvement without external expert supervision remains a central challenge in complex reasoning