Search: “reasoning”

A Dual-Process Perspective on Nudge Susceptibility in LLM-Based GUI Agents

arXiv:2609.19843v1 Announce Type: new Abstract: LLM-based GUI agents increasingly act on behalf of users in digital environments that were designed with human users in mind. These graphical user inter

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ALIBI: Adversarial Legitimacy Injection in Binary Input against LLM Malware Analyzers

DGX agent

arXiv:2609.19722v1 Announce Type: cross Abstract: Large language models are being integrated into malware triage workflows as reasoning components that summarize static evidence and produce analyst-fa

model-releasesarxiv-cs-lg

18 Sept 2026

Applications

Before the Arrest: Benchmarking LLMs on Criminal Profiling from Incomplete Evidence

arXiv:2609.19965v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly applied to legal and criminal justice tasks, yet existing work focuses almost exclusively on post-arrest s

applicationsarxiv-cs-cl

Benchmarking MLLMs via Cognitive Expected Scene Graph for Safety-Critical Visual Negation Understanding

arXiv:2609.19767v1 Announce Type: new Abstract: True machine intelligence requires transcending passive pixel registration to master top-down functional reasoning over absent information via visual ne

GAVEL: Graph World Models for Verified and Efficient Long-Horizon LLM Task Planning

arXiv:2609.19315v1 Announce Type: cross Abstract: Large language models (LLMs) provide a flexible interface for long-horizon robot planning, but generated plans often fail to respect embodiment constr

Sample Count Is Not Enough: Candidate-Generation Strategy Shapes the Energy and Performance of LLM Test-Time Scaling

model-releasesarxiv-cs-lg

arXiv:2609.19499v1 Announce Type: new Abstract: Test-time scaling can improve large language model reasoning by generating and combining multiple candidate responses. In sampling-based methods, the in

VLM-CAD: VLM-Optimized Collaborative Agent Design Workflow for Analog Circuit Sizing

arXiv:2601.07315v5 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) have demonstrated remarkable potential in multimodal reasoning. However, they can have spatial blindness and log

Decodable but Misrouted: Sparse Features Uncover a Readout Gap in Vision-Language Models for Harmful Meme Detection

arXiv:2609.18860v1 Announce Type: cross Abstract: When a large vision-language model misclassifies a harmful meme, the failure may reflect missing internal evidence or an inability to route represente

EDCT-Bench: Uncovering Faithfulness Gaps in VLMs via Explanation-Driven Counterfactual Testing

arXiv:2609.17953v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) can produce Natural Language Explanations (NLEs) that sound plausible yet remain inconsistent with the visual evidence t

Enhancing Extubation Failure Prediction with LLM-Derived Features from Respiratory Therapy Clinical Notes

arXiv:2609.17532v1 Announce Type: new Abstract: Invasive mechanical ventilation is a lifesaving therapy, but timely, safe discontinuation is essential to preventing extubation failure (EF) and related

researcharxiv-cs-cl

EviGen: Predictive Evidence Scaffolding for Verifiable Clinical Rationale Generation

arXiv:2609.18852v1 Announce Type: new Abstract: Longitudinal electronic health records (EHRs) capture years of patient history across notes, codes, labs, and procedures, and contain evidence needed to

researcharxiv-cs-cl

Hyperbolic Graph Representation Learning for Differential Diagnosis on Biomedical Knowledge Graphs

arXiv:2609.18481v1 Announce Type: new Abstract: Biomedical knowledge graphs combine ontology-derived hierarchies with transversal associations among heterogeneous entities such as phenotypes, diseases

JigSync: Gauge-Resolved Synchronization for Jigsaw Reassembly under Unknown Piece Orientation

arXiv:2609.18379v1 Announce Type: new Abstract: Square jigsaw reassembly requires recovering the spatial arrangement of shuffled fragments from their visual content and pairwise relationships. While r

Multi-Hop Knowledge Composition is Bound by Pretraining Exposure

arXiv:2606.09338v2 Announce Type: replace Abstract: Large Language Models fail at implicit multi-hop reasoning: a model answers 'When was X born?' and 'Who is Y's closest friend?' correctly but fails

researcharxiv-cs-cl

Rethinking How We Evaluate Methodological Progress in Health AI

arXiv:2609.18134v1 Announce Type: cross Abstract: Methodological progress in artificial intelligence (AI) for electronic health records (EHRs) depends on our ability to determine which algorithms work

STRETCH the Boundaries: A Unified Self-Taught Framework for Progressive LLM Evolution

model-releasesarxiv-cs-cl

arXiv:2609.18642v1 Announce Type: new Abstract: Large language models (LLMs) often suffer from capability stagnation in self-improvement training because fixed difficulty levels fail to adapt to their

Tutorials

TACTICS: Taxonomy-Aware Intelligent Corpus Sampling for Machine Translation

arXiv:2609.17956v1 Announce Type: new Abstract: Large-scale machine-translation (MT) systems are typically evaluated on random samples from a corpus whose distributional composition is an artifact of

tutorialsarxiv-cs-cl

Trajectory Learnability for Offline On-Policy Distillation with Imperfect Teachers

arXiv:2609.18321v1 Announce Type: cross Abstract: Offline on-policy distillation gains efficiency by collecting student trajectories and teacher supervision once and reusing them throughout optimizati

Tutorials

When Audit Quality Fails to Predict Downstream Utility: A Counterfactual Study of Synthetic-Data Selectors for Low-Resource African NLP

arXiv:2609.18960v1 Announce Type: new Abstract: Quality-aware synthetic-data selection rests on a proxy: examples that an LLM judge rates as good should also help a downstream model learn. In a contro

tutorialsarxiv-cs-cl

Beyond Token-Local Imitation: Reward-Compatible Temporal Credit Assignment for On-Policy Distillation

arXiv:2609.16937v1 Announce Type: cross Abstract: On-policy distillation (OPD) has emerged as an effective approach for large language model post-training, yet existing objectives face a trade-off bet

CLEAR: Cross-Source Evidence Adjudication for Large Language Models in Medicine

arXiv:2609.16301v1 Announce Type: new Abstract: Medical knowledge evolves continuously, whereas the parametric knowledge encoded in large language models (LLMs) is fixed at training time. External ret

DriveMCP: An Agentic AI framework for Advanced Driver Assistance System

arXiv:2609.17247v1 Announce Type: new Abstract: An agentic AI driver-assistance framework that integrates perception, compliance reasoning, vehicle-state interpretation, and safety arbitration into a

safetyarxiv-cs-ro

Extracting ontology-compliant knowledge from scientific text describing irradiated materials using large language models

arXiv:2609.17291v1 Announce Type: new Abstract: The quest for new materials increasingly relies on predictive models and comprehensive simulations that span scales from atomic to macroscopic levels. H

Local Ai

JustFit: 200K-Token LLM Serving on a 24 GiB Laptop with Just-in-Time State Management

arXiv:2609.17475v1 Announce Type: new Abstract: Capable open-weight models make local coding and reasoning attractive, but their context and execution state strain laptop memory. We present JustFit, a

local-aiarxiv-cs-ai

Not Another Text Benchmark: Putting the 'Visual' Back in Visual Question Answering for Large Video Models

arXiv:2609.17112v1 Announce Type: new Abstract: Large video models have exhibited impressive performance on a wide range of visual question answering tasks, owing to the rise of powerful, pretrained t

Local Ai

ProtoLIP: From Sentence-Level to Object-Level Evidence Disentanglement

arXiv:2609.16284v1 Announce Type: cross Abstract: Query-conditioned vision--language models enable fine-grained interpretation by revealing how visual evidence changes with textual queries. However, e

local-aiarxiv-cs-ai

TrafficGamer: Reliable and Flexible Traffic Simulation for Safety-Critical Scenarios with Game-Theoretic Oracles

arXiv:2408.15538v4 Announce Type: replace Abstract: While modern Autonomous Vehicle (AV) systems can develop reliable driving policies under regular traffic conditions, they frequently struggle with s

Write up on building and improving reliability of agentic harnesses + a big dataset from my work.

model-releasesr-localllama

I've been working on something for this community (and other self-hosted communities) for almost 5 months now. Personally, I would grade it as probably the closest open source equivalent of Manus and

BVB: Benchmarking Agentic Video Understanding via Programmatic Reconstruction in Blender

arXiv:2609.15478v1 Announce Type: new Abstract: Multimodal agents can create complex videos in software such as Blender by coding without relying on diffusion models. Yet video understanding benchmark

Applications

CHAI for LLMs: Improving Code-Mixed Translation in Large Language Models through Reinforcement Learning with AI Feedback

arXiv:2411.09073v4 Announce Type: replace-cross Abstract: Large language models (LLMs) show strong performance across many tasks but remain weak at understanding code-mixed (CM) language. Despite this

applicationsarxiv-cs-ai

Don't Just Look, Intervene: Perturbation Based Region Labeling for VQA Images

arXiv:2609.13228v1 Announce Type: new Abstract: Vision Language Models (VLMs) should rely on visual evidence that directly determines the correct answer, but supervision for grounding visual reasoning

Enhancing Event Candidate Acquisition for Event Linking

arXiv:2609.13670v1 Announce Type: new Abstract: Event linking associates event mentions in text with entries in a knowledge base (KB), or identifies them as out-of-KB events. Although existing methods

From Model Patterns to Abstract Semantics in Compositional Zero-Shot Learning

arXiv:2609.15649v1 Announce Type: new Abstract: Compositional Zero Shot Learning aims to recognize unseen compositions by recombining learned primitives. Recent methods rely on vision language models

Global-Local Contextual Progressive Expansion Network for Martian Landslide Segmentation in Multimodal Remote Sensing Imagery

arXiv:2609.13332v1 Announce Type: new Abstract: Automated landslide segmentation on Mars is one of the important tasks for understanding its surface processes, and all will aid in future space explora

Hindsight-Anchored Policy Optimization: Learning Through Hindsight with Thompson Sampling-Inspired Adaptive Gating

arXiv:2603.11321v3 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards improves reasoning in large language models, yet on-policy learning often suffers from cold-sta

Lexical Prompt Compression for Large Language Models: A Training-Free, Deterministic Pipeline with Empirical Pareto Analysis Across Eleven Task Categories

model-releasesarxiv-cs-cl

arXiv:2609.13154v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have made prompts increasingly large and complex. Techniques such as chain-of-thought reasoning (Wei et

Navigating Sparse Evidence: Agentic Visual RAG via Explicit Context Selection and Consolidation

arXiv:2609.15800v1 Announce Type: new Abstract: Visual Retrieval-Augmented Generation (VRAG) empowers models to navigate and answer queries about visually rich documents by retrieving relevant page im

‘Now We Can Know Everything and Do Anything,’ Jensen Huang Says at Dreamforce

model-releasesnvidia-blog

Know everything. Do anything. That was the message NVIDIA founder and CEO Jensen Huang brought to Salesforce Dreamforce Tuesday, joining CEO Marc Benioff onstage in an appearance that coincided with t

Applications

Semiotic Relations and Proof Methods: A Cross-Genre Study of Argument Structure with Large Language Models

arXiv:2609.15194v1 Announce Type: new Abstract: When a direct proof of a statement S seems hard or even impossible to obtain, there may exist another statement (or set of statements) S^{*}, somehow re

applicationsarxiv-cs-cl

STAGE: Diagnosing Semantic Transfer at Grounded Execution in Embodied Agents

model-releasesarxiv-cs-cl

arXiv:2609.13458v1 Announce Type: cross Abstract: Embodied language grounding requires more than identifying the referent of an instruction: recovered semantics must also control the action an agent e

Theseus in the Graph: Towards Traceable Multi-Hop Graph Navigation

arXiv:2609.14528v1 Announce Type: new Abstract: Multi-Hop Knowledge Graph Question Answering (KGQA) tasks require models to assemble relational evidence along paths in a KG to answer natural-language

agentsarxiv-cs-cl

Tutorials

Cognition on Graph: Navigating Massive Knowledge Space via Cognitive Cycles and Bidirectional Graph-Text Synergy

arXiv:2609.12791v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has empowered Large Language Models (LLMs) to tackle knowledge-intensive tasks. However, navigating global, heterog

tutorialsarxiv-cs-cl

14 Sept 2026

DeepSeek engineer relections on RSI - burying my talent to yesterday

model-releasesr-localllama

Note - This is translated from the actual blog link right at the bottom. A few days ago, DeepSeek v4.1 was released. It raised the ability of small models to a new level. AI is improving much faster t

14 Sept 2026

Single-Query Person-Centric Bimanual Hand-Object Interaction Detection

arXiv:2609.12155v1 Announce Type: new Abstract: Understanding person-level bi-manual interactions requires not only detecting hands, but also identifying which two hands belong to the same person and

14 Sept 2026

My experience building 64GB VRAM AI SWE assistant/agent PC

model-releasesr-localllama

As a SWE I have ultimate belief that having a irreplaceable subscription on OpenAI and Anthropic models is actually against my beliefs (I'm not fond of becoming a digital slave and being unable to do

13 Sept 2026

An Open Recipe for IMO Gold: Training Nemotron for Olympiad Mathematics

arXiv:2609.10712v1 Announce Type: new Abstract: We study how model post-training and test-time inference design affect natural-language proof generation for hard olympiad mathematics. Starting from Ne

12 Sept 2026

Extending SMT Solving with Non-Ground Clause Learning

arXiv:2609.11509v1 Announce Type: new Abstract: Quantifier instantiation is currently the main approach to non-ground SMT solving: solvers generate ground instances and solve the resulting ground SMT

12 Sept 2026

Towards a Deterministic Math Solver for Clinical Language Models

arXiv:2609.10728v1 Announce Type: new Abstract: Large language models are unreliable at arithmetic, which is a problem for clinical calculators where a single numerical error changes the recommendatio