Search: “models”

Large-scale factor analysis shows machine intelligence is only partially interpretable

arXiv:2609.36515v1 Announce Type: cross Abstract: A common assumption in language model development is that cognitive abilities are organized around a general, domain-free intelligence factor, like fl

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LexiconVLA: Learning Reusable Atomic Action Codebooks for Unseen Tasks

DGX agent

arXiv:2609.36774v1 Announce Type: new Abstract: Vision-language-action (VLA) models struggle to reuse recurring interactions in unseen tasks. Our diagnostic study reveals that reliable task completion

model-releasesarxiv-cs-ro

30 Sept 2026

LLM unbranding: Erasing Commercial Identity while Preserving Generic Utility

arXiv:2609.37127v1 Announce Type: new Abstract: Establishing unbranding as a critical practice to prevent visual logos from acquiring negative connotations is standard in image generation. Large Langu

LLMs are not stochastic parrots: Evidence for meaning-mediated abstraction from conlang-like tasks

arXiv:2609.34187v2 Announce Type: replace-cross Abstract: The strong version of the stochastic parrot argument claims that, although large language models (LLMs) may exceed rote regurgitation, they ca

researcharxiv-cs-ai

Tutorials

LLMs learn different forms of metacognition when trained to predict their own accuracy

arXiv:2609.33886v2 Announce Type: replace Abstract: Large language models are trained to always produce an answer, regardless of whether they possess the relevant knowledge, which leads them to fabric

tutorialsarxiv-cs-cl

Local Predictability and Collective Fidelity in LLM-Agent Societies

arXiv:2609.35813v1 Announce Type: cross Abstract: Compact surrogates could reduce the cost of simulating large language model societies, but must reproduce collective behavior. We compare individual p

Lookahead-R: Budget-Aware Tool Retrieval via Execution-Centric Planning

arXiv:2609.35811v1 Announce Type: cross Abstract: Tool retrieval is a critical bottleneck for LLM-based agents operating over large, heterogeneous API ecosystems. Existing approaches face an inherent

Memory Consolidation Flattens the Temporal Shape of User Facts

arXiv:2609.36457v1 Announce Type: new Abstract: Long-term memory systems turn conversations into short stored notes. A note can keep a user fact while losing evidence about whether the fact still hold

Momentum-Coupled Rubric Adaptation for Detailed Image Captioning

arXiv:2609.36893v1 Announce Type: new Abstract: Detailed image captioning requires accurate and comprehensive descriptions of fine-grained visual content, yet caption quality spans factual accuracy, i

MUGEN: Interactive Panoramic World Exploration via Camera Control

arXiv:2609.38077v1 Announce Type: new Abstract: Interactive panoramic video generation aims to synthesize immersive 360extdegree{} videos that remain visually coherent while following user-specified c

Normative Loss Landscape Navigation: A Trajectory-Based Approach to Mitigating Forgetting in Incremental Learning

arXiv:2609.35926v1 Announce Type: cross Abstract: Continual learning models suffer from catastrophic forgetting when trained sequentially on non-stationary data distributions. Previously, this has bee

OptiCom : A Unified Framework for State-Conditioned Composition in LLM-Driven Optimization

arXiv:2609.37221v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed to solve complex scientific and practical problems via iterative optimization. However, dynamical

ORMA: Optimization-based Monocular 4D Reconstruction of Articulated Animals

arXiv:2609.37986v1 Announce Type: new Abstract: Recovering articulated 4D representations of animals from monocular videos remains challenging due to the large diversity of quadruped morphologies and

POET: Preference Optimization for Enhanced Text-to-Image Generation

arXiv:2510.12041v3 Announce Type: replace Abstract: Recent advances in text-to-image (T2I) generation have achieved impressive results, yet existing models often struggle with simple or underspecified

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QuantMLA: Function-Aligned Dual-Path Quantization for Low-Bit MLA KV Caching

arXiv:2609.36760v1 Announce Type: cross Abstract: Multi-Head Latent Attention (MLA) enables expressive multi-head attention with compact caches for its content and decoupled RoPE paths, yet cache memo

RawVLA: Embodied Neural Image Signal Processor For Robotic Manipulation

arXiv:2609.37530v1 Announce Type: cross Abstract: Vision-language-action (VLA) models typically operate on RGB images produced by a fixed camera image signal processor (ISP), leaving the imaging pipel

ReCAP: Retrieval-Guided Capability Reuse for Multimodal Continual Instruction Tuning

arXiv:2609.37889v1 Announce Type: new Abstract: Multimodal continual instruction tuning (MCIT) aims to enable multimodal large language models to acquire new capabilities from sequential tasks while p

Reliable but Design-Sensitive: Instrument Uncertainty in LLM Annotation

arXiv:2609.35824v1 Announce Type: new Abstract: Large language models (LLMs) can give reliable labels under one setup yet change those labels when researchers make other reasonable design choices. We

researcharxiv-cs-cl

Relic: From Multi-Agent Collaboration to Persistent Organizational Capability

arXiv:2609.32965v2 Announce Type: replace Abstract: Multiple agents may often conflict in an organization: for example, one coding agent changes an interface in a repository, but another continues to

Resolving the Missing Financial Data Crisis: A Generative AI Pipeline for SEC 10-K Extraction

arXiv:2609.35864v1 Announce Type: new Abstract: SEC 10-K filings contain substantial financial information that is not consistently captured in structured datasets, creating a missing-data problem aff

Retrieval Capacity of Self-Attention Under Competition

arXiv:2609.37879v1 Announce Type: new Abstract: How many tokens from its context does a language model actually use, and what determines that number? We study this question through self-attention. Wit

researcharxiv-cs-cl

Routing in Gradient Space: Balanced Usage Is Not Expert Specialization

arXiv:2609.36724v1 Announce Type: new Abstract: Sparse expert models can distribute traffic evenly while still grouping incompatible training signals within the same experts. We study routing as a gra

RS-OPSD: Reliable Privileged On-Policy-Self-Distillation for Ultra-High-Resolution Remote Sensing VQA

arXiv:2609.38072v1 Announce Type: new Abstract: Ultra-high-resolution (UHR) remote sensing visual question answering (VQA) requires models to resolve small visual evidence within extremely large image

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SAGE: A Statistical Acceptance Gate for Self-Evolving Agents

arXiv:2609.36043v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents increasingly self-evolve by editing a persistent skill document that encodes their workflow, tool-use rules, and

Selecting The Most Informative Tokens in Natural Language Autoencoders

arXiv:2609.37040v1 Announce Type: cross Abstract: Natural language autoencoders translate a language model's internal activations into readable explanations. Explaining every token position is costly.

researcharxiv-cs-ai

Self-discovering RL in the Era of Experience: Is Learning History an Asset or a Burden?

arXiv:2609.35897v1 Announce Type: new Abstract: The pursuit of recursive self-improvement (RSI) toward general intelligence is divided between macro-level language model scaling and the interaction-dr

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

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

Storage Is Not Strategy: State-Conditioned Support Control for LLM Unlearning

arXiv:2609.37858v1 Announce Type: cross Abstract: Many localized large language model (LLM) unlearning methods select a small parameter subset from a localization signal and keep it fixed during optim

Technical note on: Zero-Training Feature-Space Alignment via Information Geometry

arXiv:2609.37302v1 Announce Type: new Abstract: Deep vision models often degrade under distribution shift. Test-time adaptation can improve robustness but typically requires iterative optimization, hy

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ThinQuant: Scalable Rotation Learning for Weight and Activation Quantization of LLMs

arXiv:2609.36120v1 Announce Type: cross Abstract: Learned rotations play an important role in enabling low-bit weight and activation quantization of large language models by smoothing outliers in the

TRACE: Deployable Tree-Relational Structure Enhancement for Oncology LLMs

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

Agents

Training LLMs to Verbalize Evaluation Awareness

arXiv:2609.36316v1 Announce Type: cross Abstract: Evaluation awareness (EA) can cause large language models (LLMs) to behave differently during audits than in deployment, yet measuring and accounting

agentsarxiv-cs-ai

UserProxyBench: Evaluating LLM User Simulators for Agent Benchmarks and Training

arXiv:2609.38043v1 Announce Type: new Abstract: Interactive agent benchmarks and multi-turn reinforcement learning increasingly place a second language model in the role of the user. This simulated us

Video2STL: Grounding VLM-Generated Temporal Specifications for Robot Learning

arXiv:2609.37519v1 Announce Type: cross Abstract: Video-based policy learning is particularly promising, as it illustrates target behaviors without requiring action annotations or embodiment-matched d

What Comes Next? Omni-StoryBench for Evaluating Story-Grounded Omnimodal Generation

arXiv:2609.37317v1 Announce Type: new Abstract: Omnimodal evaluation should go beyond independent text, image, and speech production: individually plausible outputs may not express a coherent shared e

When Should LLMs Trust Their Own Revisions? A Risk-Aware Study of Intrinsic Self-Correction

arXiv:2609.35832v1 Announce Type: cross Abstract: Intrinsic self-correction asks a language model to revise its own answer without receiving new external evidence. A second pass can recover mistakes,

When to Adapt: Multi-Signal Domain Shift Detection for Efficient Training-Free Adaptation in Open-Vocabulary Segmentation

arXiv:2609.37602v1 Announce Type: cross Abstract: Robust and reliable perception is essential for autonomous robots operating in real-world environments, particularly in long-term missions where envir

Which Attention Heads are like the Human Head? Not the Ones that Compute

arXiv:2609.37991v1 Announce Type: new Abstract: Brain-AI alignment is often interpreted as a sign that model and brain perform similar computations. Whether the aligned units are causally involved in

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Who Warmed the Archives? LLMs Overestimate Historical Warmth

arXiv:2609.37499v1 Announce Type: new Abstract: Historical archives are an under-used source for extending the instrumental climate record backward in time, and LLMs offer a way to extract the indices

A Light Bilevel Refinement Aligns Self-Supervised Representations for Stronger Task-Specific Learning

arXiv:2609.33424v1 Announce Type: cross Abstract: Self-supervised pretraining learns representations that are broadly transferable across downstream tasks, yet direct fine-tuning can be suboptimal due

Local Ai

A Solvable Theory of Pre-training Data Poisoning: Regime-Dependent Scaling Exponents

arXiv:2609.32288v1 Announce Type: new Abstract: Pre-training data poisoning of large language models is usually studied using targeted backdoors and their survival through safety post-training, which

local-aiarxiv-cs-lg

A Unified Optimism-Agnostic Framework for Linear Bandits over Spherical Action Sets

arXiv:2609.32149v1 Announce Type: cross Abstract: Linear bandits model sequential decision-making problems with noisy rewards that are linear in the decision variable, where an agent must simultaneous

A Unifying Framework of Concept-based Explainable AI with Completeness Guarantees

arXiv:2609.34750v1 Announce Type: cross Abstract: Concept-based explanations describe neural network predictions through human-understandable properties of inputs called concepts. The field encompasse

researcharxiv-cs-cv

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

ALICE: In-context, Zero-shot, Mutual Information Estimation

arXiv:2609.34962v1 Announce Type: new Abstract: Estimating mutual information (MI) from samples is a central objective in a variety of scientific fields. Modern neural estimators are accurate in the l

Local Ai

AnswerMap: Faithful Spatial Interpretability of VLMs from Answer Posteriors

arXiv:2609.35247v1 Announce Type: new Abstract: When a VLM answers a visual query, current interpretability tools rely on text rationales, which use a mismatched modality, or on internal read-outs, wh

local-aiarxiv-cs-cv

Automated Species Identification in Camera Trap Images for Wildlife Conservation

arXiv:2609.35420v1 Announce Type: new Abstract: Wildlife conservation involves protecting, preserving, and managing wildlife species and their habitats. With today's rapid pace of human development, c

Background Gradients Shape Memorization in Flow Matching

arXiv:2609.33210v1 Announce Type: new Abstract: Repetition is closely associated with memorization in generative models, but how other training images affect the retention and copying of targets remai