Search: “models”

Behavioral Capacity Certificates for Quantized Language Models

arXiv:2609.37887v1 Announce Type: new Abstract: Activation and key-value cache precision change what a quantized language model computes without altering its stored weights. Direct weight-code bounds,

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Beyond Semantics: How Temporal Biases Shape Retrieval in Transformer and State-Space Models

DGX agent

arXiv:2510.22752v2 Announce Type: replace-cross Abstract: In-context learning is governed by both temporal and semantic relationships, shaping how Large Language Models (LLMs) retrieve contextual info

safetyarxiv-cs-ai

30 Sept 2026

Cross-attention encoding models reveal dynamic spatiotemporal routing across human higher visual cortex

arXiv:2609.36366v1 Announce Type: cross Abstract: Understanding how the brain parses actions and events from time-varying natural inputs is a central challenge in neuroscience. Recent work has used de

researcharxiv-cs-ai

DatalogBench: Evaluating Large Language Models on Text-to-Datalog Synthesis

arXiv:2609.37233v1 Announce Type: cross Abstract: Datalog underpins reasoning tasks such as program analysis, but its programs are hard to write. Existing synthesizers automate this task but require u

Distilling What Matters: Confidence-Aware Selective Distillation for Large Language Models

arXiv:2609.36734v1 Announce Type: new Abstract: Knowledge Distillation (KD) trains a smaller-capacity student model to imitate a larger-capacity teacher model by matching output distributions, implici

safetyarxiv-cs-cl

Dyad: Extending Large Language Models with Native Typed Decision-Making

arXiv:2609.36116v1 Announce Type: new Abstract: We study how to build more capable general-purpose agents by extending large language models (LLMs) with native typed decision-making. We introduce Dyad

HEAR: Real Voices, Real Bias: A Large-Scale Human-Recorded, Demographically Diverse Benchmark for Audio Language Models

arXiv:2609.35952v1 Announce Type: cross Abstract: We introduce HEAR (Human-recorded Evaluation of Audio-LLM bias by Real speakers), a large-scale, ecologically valid benchmark comprising 87k real huma

MetaCtrl: Your Large Language Models Can Reason Better and More Concisely with a Metacognitive Controller

arXiv:2609.37304v1 Announce Type: new Abstract: Large reasoning models improve performance on challenging problems by allocating additional computation before answering, but longer reasoning does not

OmniRoute: Mapping Temporal Semantic Evidence to Audio-Visual Token Budgets for Efficient Omnimodal Large Language Models

arXiv:2609.37052v1 Announce Type: new Abstract: Omnimodal large language models (Omni-LLMs) encode audio and visual streams into temporally interleaved token sequences for multimodal reasoning. Howeve

Rho: A Foundation for Efficiently Adaptable VLA Models

arXiv:2609.38164v1 Announce Type: new Abstract: General-purpose physical AI models must combine broad visual and linguistic capabilities with precise control across robot embodiments and efficient ada

safetyarxiv-cs-ro

S^3: Spectral Null-Space Swap Makes Reasoning Models Efficient

arXiv:2609.37976v1 Announce Type: cross Abstract: LLMs trained with Chain-of-thought excel in reasoning capability, but often come with excessive token cost. We find that the core of reasoning capacit

researcharxiv-cs-ai

Selective Channel Restoration for Backdoored Vision-Language Models

arXiv:2609.37759v1 Announce Type: cross Abstract: Vision-language models (VLMs) exhibit strong multimodal capabilities but remain vulnerable to backdoors implanted through poisoned fine-tuning data. E

ThinkingGuard: Decoding Implicit Hazards via Step-by-Step Risk Attribution in Multimodal Large Language Models

arXiv:2609.36562v1 Announce Type: cross Abstract: While Multimodal Large Language Models (MLLMs) are increasingly deployed in safety-critical domains, their reliability is threatened by multimodal imp

safetyarxiv-cs-ai

What Makes Recurrence Effective in Looped Language Models?

model-releasesarxiv-cs-cl

arXiv:2609.36636v1 Announce Type: cross Abstract: Looped language models (LoopLMs) increase computational depth through parameter sharing, offering a path to scale inference computation without adding

A Comparative Analysis of Attention versus State-Space Models for In-Context Learning

arXiv:2609.32341v1 Announce Type: new Abstract: Transformers and state-space models (SSMs) are two prominent sequential learning architectures, yet their comparison remains largely empirical and exist

Applications

ActionUNet: Improving Robustness of VLA Models with Efficient Multi-scale Fine-tuning

arXiv:2609.34982v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown great promise for robotic manipulation by mapping multi-modal semantics to physical actions. However, thi

applicationsarxiv-cs-cv

Can Motion-Language Models Ground Structure? STRIDE for Evaluating the Evaluators

arXiv:2609.32462v1 Announce Type: new Abstract: Motion-language models are typically scored by motion-language evaluators, but how well these evaluators ground language structure remains unclear. Here

Continual Data Unlearning in Diffusion Models via Transition-based Regularization

arXiv:2609.32328v1 Announce Type: new Abstract: Data unlearning in diffusion models aims to remove the influence of specific training examples without suppressing the broader concepts they represent.

Agents

CyberWorld: World Models for Sample-Efficient Autonomous Cyber Defense

arXiv:2609.31893v1 Announce Type: new Abstract: Deep reinforcement learning has become a prominent approach to autonomous cyber defense. Existing methods are predominantly model-free and consequently

agentsarxiv-cs-lg

Don't Forget! Decomposing the Training Dynamics of Memorization in Language Models

arXiv:2609.34933v1 Announce Type: new Abstract: Memorization has been proposed as a mechanism to explain how language models fit the tail of their training distributions, but its training dynamics are

How Strong Is the Evidence for the Artificial Hivemind? Reevaluating Evidence for the Open-Ended Homogeneity of Language Models

arXiv:2609.33936v1 Announce Type: new Abstract: Recent research argues that language models exhibit pronounced homogeneity in open-ended generation, framing such behavior as an Artificial Hivemind tha

Learning Transferable Sensor Models via Language-Informed Pretraining

arXiv:2603.11950v2 Announce Type: replace-cross Abstract: Multimodal language models have demonstrated strong semantic understanding and reasoning over physiological and behavioral signals captured fr

On Memory: A comparison of memory mechanisms in world models

arXiv:2512.06983v2 Announce Type: replace-cross Abstract: World models enable agents to plan within imagined environments by predicting future states conditioned on past observations and actions. Howe

Refresh or Realize? Compute Allocation in Drifting Models

arXiv:2609.32298v1 Announce Type: new Abstract: Drifting Models train a one-step generator by recomputing a finite-sample drift field at every iteration and taking an optimizer step toward the drifted

Source Anchoring for Physical Consistency in Flow Matching Models

arXiv:2609.33510v1 Announce Type: new Abstract: Deep generative models are used to solve partial differential equations and model distributions of physical system states, but ensuring that the generat

Training-Free Uncertainty Estimation for Embedding Models

arXiv:2306.00206v3 Announce Type: replace Abstract: Embedding models, often obtained via self-supervised learning, extract general-purpose representations from data. Quantifying the reliability of the

WM-VLM: Probing Internal World Models for Interleaved Visual-Textual Reasoning

arXiv:2609.34826v1 Announce Type: new Abstract: Humans often solve spatial problems by mentally simulating visual transformations. In contrast, conventional vision-language models (VLMs) reason primar

researcharxiv-cs-cv

WorldGuide: Learning Success-Failure Boundaries in Latent World Models for Vision-Language-Action Policies

arXiv:2609.34206v1 Announce Type: new Abstract: Latent world models offer a promising way to improve Vision-Language-Action policies by capturing the consequences of actions. However, models trained p

safetyarxiv-cs-cv

Beyond Bag-of-Words: Diagnosing Compositional Binding Failures in Vision-Language Models

arXiv:2602.02043v2 Announce Type: replace-cross Abstract: Modern vision-language models struggle with basic compositional reasoning, failing to bind attributes to objects or relations to their referen

researcharxiv-cs-ai

Don't CLAP: Are Music-Text Models Bag-of-Words?

arXiv:2609.30540v1 Announce Type: cross Abstract: Text-to-music systems are assessed on audio quality and on how faithfully the music follows its prompt, and the CLAP score, the cosine similarity betw

researcharxiv-cs-cl

In-Context Binding Capacity in Language Models

arXiv:2609.30634v1 Announce Type: new Abstract: How many assignments can a language model recall before it loses track of which value belongs to which entity? We measure this limit using continuous re

Learning Natural Conversational Behavior in Tandem Speech-to-Speech Models with Randomized Guidance

arXiv:2609.30773v1 Announce Type: new Abstract: Tandem speech-to-speech architectures couple a responsive speech frontend with an asynchronous text backend. In KAME, a large language model (LLM) serve

researcharxiv-cs-cl

ProCAP: Probabilistic Cross-Attentive Prompt Learning for Vision-Language Models

arXiv:2609.30434v1 Announce Type: new Abstract: Pre-trained vision-language models such as CLIP can recognize new categories via prompting, but they often struggle when labeled data are scarce or the

researcharxiv-cs-cv

Reinforcement Learning of Communication in a Mesh of Small Language Models

arXiv:2609.30578v1 Announce Type: new Abstract: Language models gain accuracy from more compute at test time, but majority voting over independent samples saturates: as samples grow, the vote converge

RupeeBias: Auditing Demographic Bias in Indian Economic Guidance from Large Language Models

model-releasesarxiv-cs-cl

arXiv:2609.31245v1 Announce Type: new Abstract: Individuals turn to large language models (LLMs) for guidance across a wide range of economic tasks, from comparing loan options and planning savings to

Stepwise Intrinsic Rewards for Reasoning in Large Language Models

arXiv:2602.01034v2 Announce Type: replace Abstract: Reinforcement learning (RL) has become a widely used paradigm for improving the reasoning abilities of large language models (LLMs) and Vision-langu

safetyarxiv-cs-ai

Automatic Rank Allocation for Low-Rank Adaptation in Large Language Models via lp Regularization

arXiv:2609.28998v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) has become a popular parameter-efficient fine-tuning method for large language models. A key challenge in LoRA is how to dete

Applications

DeltaWAM: Delta World Action Models for Bimanual Manipulation

arXiv:2609.28811v1 Announce Type: new Abstract: World-action models (WAMs) transfer visual and motion priors from pretrained video generators to robot control by jointly modeling visual dynamics and a

applicationsarxiv-cs-cv

Agents

Driving Epidemic Models with AI Agents: the Epydemix Agent Framework

arXiv:2609.28692v1 Announce Type: new Abstract: Artificial Intelligence agents based on large language models provide convenient natural language interfaces to scientific software, but reliability is

agentsarxiv-cs-ai

Local Ai

Minimally Invasive Steering of Language Models

arXiv:2609.30218v1 Announce Type: cross Abstract: Pre-logit steering adapts a frozen language model to a test-time reward by adding vectors to its final hidden states. Unregularized reward optimizatio

local-aiarxiv-cs-ai

Retrieve-to-Localize: Bridging Large Language Models and LiDAR Geometry for Spatial Grounding

arXiv:2609.29835v1 Announce Type: new Abstract: LiDAR provides precise geometric information for spatial perception tasks such as object detection in autonomous driving and outdoor robotics. However,

Reward-Tilted On-Policy Distillation for Acoustic Grounding in Audio-Language Models

arXiv:2609.28778v1 Announce Type: cross Abstract: Audio-language models (ALMs) can exploit textual shortcuts to answer questions while overlooking acoustic evidence, weakening audio understanding. On-

safetyarxiv-cs-cl

Discover, Falsify, Revise: Auditing Input-Use Claims from Source Code to Predictive Contribution in Agent-Discovered Cell Models

arXiv:2609.27234v1 Announce Type: new Abstract: AI virtual cells aim to predict cellular responses to specified interventions, yet held-out predictive performance alone does not establish use of the s

Distilling Sequential Computation in Transformer Language Models

arXiv:2609.27233v1 Announce Type: new Abstract: Transformer language models process sequences token by token in an autoregressive manner, making growing contexts increasingly expensive. Yet many adjac

researcharxiv-cs-cl

Do Center Biases Propagate? Robustness of Pathology Foundation Models in Whole-Slide Image Classification

arXiv:2609.28231v1 Announce Type: cross Abstract: Pathology foundation models (PFMs) have transformed computational pathology through powerful representation learning from histopathological images. PF

Hardware

Does Step Law Transfer to Small-Scale Language Models? An Empirical Recalibration Below 59M Parameters

arXiv:2609.27581v1 Announce Type: cross Abstract: Step Law gives power-law formulas for the optimal peak learning rate eta* and batch size B* when pre-training language models. It was calibrated on mo

hardwarearxiv-cs-cl

Same Scores, Different Decisions: Evaluating JEV and Language Models for Legal Document Understanding

model-releasesarxiv-cs-cl

arXiv:2609.27678v1 Announce Type: new Abstract: Contract inference requires multiple judgments about a shared document, but aggregate accuracy can conceal changes in the individual decisions. Repeated

Bayesian Fusion of Active Contour Models and ConvNet Priors for Standing Dead Tree Segmentation

arXiv:2410.07421v2 Announce Type: replace Abstract: Instance segmentation is a core computer vision task with great practical significance. Recent advances, driven by large-scale benchmark datasets, h