Daily Arxiv

This page organizes papers related to artificial intelligence published around the world.
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ACCeLLiuM: Supervised Fine-Tuning for Automated OpenACC Pragma Generation

AnchDrive: Bootstrapping Diffusion Policies with Hybrid Trajectory Anchors for End-to-End Driving

Diffusion-Augmented Contrastive Learning: A Noise-Robust Encoder for Biosignal Representations

FusedANN: Convexified Hybrid ANN via Attribute-Vector Fusion

HiCoLoRA: Addressing Context-Prompt Misalignment via Hierarchical Collaborative LoRA for Zero-Shot DST

A Longitudinal Randomized Control Study of Companion Chatbot Use: Anthropomorphism and Its Mediating Role on Social Impacts

TimeMosaic: Temporal Heterogeneity Guided Time Series Forecasting via Adaptive Granularity Patch and Segment-wise Decoding

Automated Facility Enumeration for Building Compliance Checking using Door Detection and Large Language Models

Dendritic Resonate-and-Fire Neuron for Effective and Efficient Long Sequence Modeling

Comparing RAG and GraphRAG for Page-Level Retrieval Question Answering on Math Textbook

RPG: A Repository Planning Graph for Unified and Scalable Codebase Generation

Distribution-Aligned Decoding for Efficient LLM Task Adaptation

DivLogicEval: A Framework for Benchmarking Logical Reasoning Evaluation in Large Language Models

Recent Advances in Microscopy Image Enhancement using Deep Learning: A Survey

Constructive Conflict-Driven Multi-Agent Reinforcement Learning for Strategic Diversity

Towards a Physics Foundation Model

Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews

Positional Encoding via Token-Aware Phase Attention

Chain or tree? Re-evaluating complex reasoning from the perspective of a matrix of thought

A Two-Stage Strategy for Mitosis Detection Using Improved YOLO11x Proposals and ConvNeXt Classification

JudgeAgent: Knowledge-wise and Dynamic LLM Evaluation with Agent-as-Interviewer

Draw-In-Mind: Rebalancing Designer-Painter Roles in Unified Multimodal Models Benefits Image Editing

Scalable Option Learning in High-Throughput Environments

“She was useful, but a bit too optimistic”: Augmenting Design with Interactive Virtual Personas

In-Context Algorithm Emulation in Fixed-Weight Transformers

Dream to Chat: Model-based Reinforcement Learning on Dialogues with User Belief Modeling

Sparse but Wrong: Incorrect L0 Leads to Incorrect Features in Sparse Autoencoders

Conflict-Aware Soft Prompting for Retrieval-Augmented Generation

ERIS: An Energy-Guided Feature Disentanglement Framework for Out-of-Distribution Time Series Classification

StreetReaderAI: Making Street View Accessible Using Context-Aware Multimodal AI

Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning

Intuition emerges in Maximum Caliber models at criticality

GTPO and GRPO-S: Token and Sequence-Level Reward Shaping with Policy Entropy

Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Models

Decentralized Aerial Manipulation of a Cable-Suspended Load using Multi-Agent Reinforcement Learning

SpectrumWorld: Artificial Intelligence Foundation for Spectroscopy

DAMR: Efficient and Adaptive Context-Aware Knowledge Graph Question Answering with LLM-Guided MCTS

Generative Logic: A New Computer Architecture for Deterministic Reasoning and Knowledge Generation

Persona-Augmented Benchmarking: Evaluating LLMs Across Diverse Writing Styles

Hierarchical Graph Neural Network for Compressed Speech Steganalysis

R-Stitch: Dynamic Trajectory Stitching for Efficient Reasoning

The Invisible Leash: Why RLVR May or May Not Escape Its Origin

APTx Neuron: A Unified Trainable Neuron Architecture Integrating Activation and Computation

LoopServe: An Adaptive Dual-phase LLM Inference Acceleration System for Multi-Turn Dialogues

Rethinking the Embodied Gap in Vision-and-Language Navigation: A Holistic Study of Physical and Visual Disparities

KV Cache Steering for Controlling Frozen LLMs

Lightweight MSA Design Advances Protein Folding From Evolutionary Embeddings

Learn Globally, Speak Locally: Bridging the Gaps in Multilingual Reasoning

Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions

Investigating Redundancy in Multimodal Large Language Models with Multiple Vision Encoders

Neural-Network solver of ideal MHD equilibria

Why Reinforcement Fine-Tuning Enables MLLMs Preserve Prior Knowledge Better: A Data Perspective

Beyond Simple Graphs: Neural Multi-Objective Routing on Multigraphs

On the Necessity of Output Distribution Reweighting for Effective Class Unlearning

TAMMs: Temporal-Aware Multimodal Model for Satellite Image Change Understanding and Forecasting

Latent Concept Disentanglement in Transformer-based Language Models

Personalized LLM Decoding via Contrasting Personal Preferences

Exploiting Block Coordinate Descent for Cost-Effective LLM Model Training

Security Degradation in Iterative AI Code Generation -- A Systematic Analysis of the Paradox

Think With Videos For Agentic Long-Video Understanding

VidBridge-R1: Bridging QA and Captioning for RL-based Video Understanding Models with Intermediate Proxy Tasks

Position: Simulating Society Requires Simulating Thought

AMPED: Adaptive Multi-objective Projection for balancing Exploration and skill Diversification

DriveAction: A Benchmark for Exploring Human-like Driving Decisions in VLA Models

Resisting Contextual Interference in RAG via Parametric-Knowledge Reinforcement

Dual Branch VideoMamba with Gated Class Token Fusion for Violence Detection

CapSpeech: Enabling Downstream Applications in Style-Captioned Text-to-Speech

Physics-Guided Motion Loss for Video Generation Model

Probing Neural Topology of Large Language Models

InfiMed: Low-Resource Medical MLLMs with Advancing Understanding and Reasoning

Mamba Integrated with Physics Principles Masters Long-term Chaotic System Forecasting

Model-Preserving Adaptive Rounding

DORAEMON: Decentralized Ontology-aware Reliable Agent with Enhanced Memory Oriented Navigation

SDPO: Importance-Sampled Direct Preference Optimization for Stable Diffusion Training

Spectral-inspired Operator Learning with Limited Data and Unknown Physics

BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge Bases

Beyond the Proxy: Trajectory-Distilled Guidance for Offline GFlowNet Training

Prompting is not Enough: Exploring Knowledge Integration and Controllable Generation on Large Language Models

HD-PiSSA: High-Rank Distributed Orthogonal Adaptation

Can LLMs Alleviate Catastrophic Forgetting in Graph Continual Learning? A Systematic Study

FFT-based Dynamic Subspace Selection for Low-Rank Adaptive Optimization of Large Language Models

From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning

BP-Seg: A graphical model approach to unsupervised and non-contiguous text segmentation using belief propagation

Bottlenecked Transformers: Periodic KV Cache Consolidation for Generalized Reasoning

The Polar Express: Optimal Matrix Sign Methods and Their Application to the Muon Algorithm

Unlearning Isn't Deletion: Investigating Reversibility of Machine Unlearning in LLMs

Learning Flexible Forward Trajectories for Masked Molecular Diffusion

Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems

Attributing Response to Context: A Jensen-Shannon Divergence Driven Mechanistic Study of Context Attribution in Retrieval-Augmented Generation

UniErase: Towards Balanced and Precise Unlearning in Language Models

Octic Vision Transformers: Quicker Visions Through Equivariance

Intentional Gesture: Deliver Your Intentions with Gestures for Speech

UltraEdit: Training-, Subject-, and Memory-Free Lifelong Editing in Language Models

VocalAgent: Large Language Models for Vocal Health Diagnostics with Safety-Aware Evaluation

Learning Hierarchical Domain Models Through Environment-Grounded Interaction

Shadow-FT: Tuning Instruct Model via Training on Paired Base Model

Structured Relational Representations

Latent Veracity Inference for Identifying Errors in Stepwise Reasoning

Feature Hedging: Correlated Features Break Narrow Sparse Autoencoders

ZeroTuning: Unlocking the Initial Token's Power to Enhance Large Language Models Without Training

From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning

Created by

Haebom

Author

Chen Shani, Liron Soffer, Dan Jurafsky, Yann LeCun, Ravid Shwartz-Ziv

Outline

This study applies the information bottleneck principle to compare LLMs with humans in balancing compression and semantic preservation. By analyzing and comparing embeddings from over 40 LLMs with human categorization benchmarks, we reveal differences between LLMs and humans.

Takeaways, Limitations

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Takeaways:

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LLM roughly matches human categories, but misses fine-grained semantic distinctions important for human understanding.

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LLMs perform aggressive statistical compression to achieve “optimal” information-theoretic efficiency, whereas humans prioritize contextual richness and adaptive flexibility.

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The encoder model outperforms the decoder model in human alignment.

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Conceptual structures develop in distinct stages, with initial rapid formation and structural reorganization, and semantic processing moving from deep layers to intermediate network layers as the model discovers more efficient encodings.

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Limitations:

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LLM optimizes compression, while humans optimize adaptive usability, revealing a fundamental difference between artificial intelligence and biological intelligence.

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