Search: “reasoning”

Nameless Tokenization: A Lossless Tokenizer-Level Defense Against Control-Token Forgery in Open-Weight LLMs

arXiv:2609.16984v1 Announce Type: new Abstract: Open-weight language models publish the strings their chat templates use to mark turns, roles and tool results, which the tokenizer maps back to the res

agentsarxiv-cs-cl

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Neuro-Symbolic Hierarchical Intention Anticipation in Human Behavior

DGX agent

arXiv:2609.17064v1 Announce Type: new Abstract: Assistive autonomous systems must anticipate human goals before an observed behavior is complete. This article formulates anticipation as goal inference

model-releasesarxiv-cs-ai

16 Sept 2026

Never Stop Thinking: Continuous-Time Language Agents

arXiv:2609.17416v1 Announce Type: new Abstract: Voice agents built on LLMs follow a rigid listen-think-speak loop that inserts seconds of dead air before every reply. We show that continuous-time cogn

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Tutorials

On the Importance of Gating: Memorization vs. In-Context Learning in State Space Models

arXiv:2609.16540v1 Announce Type: cross Abstract: State Space Models (SSMs) have emerged as a compelling alternative to Transformers, enabling sequence modeling with constant memory and linear compute

tutorialsarxiv-cs-ai

PARSA-Bench: A Comprehensive Persian Audio-Language Model Benchmark

model-releasesarxiv-cs-cl

arXiv:2603.14456v2 Announce Type: replace Abstract: Persian poses unique audio understanding challenges through its classical poetry, traditional music, and pervasive code-switching, none of which is

PiPS: Post-Hoc Prototypical Explanations for Interpretable Semantic Segmentation

arXiv:2609.16909v1 Announce Type: new Abstract: With the increasing deployment of deep neural networks in critical systems, such as medical diagnostics and autonomous vehicles, ensuring their interpre

local-aiarxiv-cs-cv

POSPAN: Position-Constrained Span Masking for Language Model Pre-training

arXiv:2609.16061v1 Announce Type: cross Abstract: Span-level masked language modeling (MLM) has shown to be advantageous to pre-trained language models over the original single-token MLM, as entities/

Risk-Calibrated Bayesian Streaming Intrusion Detection with SRE-Aligned Decisions

arXiv:2510.09619v2 Announce Type: replace-cross Abstract: [Corrected v2: an audit found that the score, threshold, and latency descriptions below are not what the shared codebase implements, and that

researcharxiv-cs-lg

Same Answer, Different Representations: Hidden instability in VLMs

arXiv:2602.06652v2 Announce Type: replace Abstract: The robustness of Vision Language Models (VLMs) is commonly assessed through output-level invariance, implicitly assuming that stable predictions re

Smarter by the Moment: Environment-Driven Dynamic Policies for Continual LLM Improvement

arXiv:2609.16800v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable progress across diverse domains, but continual adaptation to evolving tasks and environments remai

safetyarxiv-cs-cl

Structural Negative Transfer in Federated Graph Neural Networks: Diagnosis, Causal Investigation, and the Limits of Divergence-Aware Mitigation

arXiv:2609.16977v1 Announce Type: new Abstract: Federated learning lets multiple participants train a shared model without pooling raw data, by exchanging locally trained model updates instead. Federa

local-aiarxiv-cs-lg

SWB-DM: A Calibrated Sliced-Wasserstein-Barycenter Aggregator with Delayed-Momentum Caching for Byzantine-Robust Federated Learning under Partial Participation

arXiv:2609.16099v1 Announce Type: new Abstract: Robust aggregation methods for federated learning quietly rest on a fragile assumption: that whoever shows up in a given round is a fair sample of the f

researcharxiv-cs-lg

Symmetry-Aware Likelihood-Orbit Aggregation for Selective Left-Right Claim Verification

arXiv:2609.17004v1 Announce Type: new Abstract: Frozen vision-language models (VLMs) remain unreliable on fine-grained left-right claims, and raw claim likelihoods need not reliably rank verification

researcharxiv-cs-cv

Universal Defenses for Tool-Integrated LLM Agents Against Adversarial Attacks

arXiv:2609.16098v1 Announce Type: cross Abstract: Large Language Model (LLM) agents have demonstrated impressive capabilities across a variety of domains, particularly when integrated with external to

ViCo: Visual-oriented Coding with Self-Reflection for Chart Replication

arXiv:2609.16014v1 Announce Type: new Abstract: This paper addresses the challenge of generating high-quality academic charts that match the visual standards of human-authored papers. While existing A

researcharxiv-cs-cl

ViD: Vision-Dominant Gender Bias Mitigation for Large Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2609.16647v1 Announce Type: new Abstract: Gender bias in large vision-language models (LVLMs) undermines their fairness and reliability, compromising output trustworthiness. Current mitigation m

VideoMM: Adaptive Macro-Micro Inference for Efficient Video MLLMs

arXiv:2609.16722v1 Announce Type: new Abstract: Scaling Multimodal Large Language Models (MLLMs) to long-form video understanding is bottlenecked by the explosion of visual tokens, which saturates con

World Model Science: Self-Organized Criticality, Weak Chaos, and Metastable Belief Dynamics in Long-Horizon LLM Agents

arXiv:2609.17419v1 Announce Type: new Abstract: Long-horizon LLM agents must maintain task state across extended sequences of observations, actions, tool calls, and intermediate beliefs. We study thes

You Don't Need To Train: Agentic Heuristic Learning Studio for Executable Human Activity Recognition

arXiv:2609.16065v1 Announce Type: cross Abstract: Human activity recognition (HAR) is usually framed as gradient-based training of neural networks. Agentic Heuristic Learning (AHL) Studio explores a c

Zero-shot narrative detection in social messaging

arXiv:2609.17310v1 Announce Type: new Abstract: This study investigates the zero-shot ability of large language models (LLMs) to identify and classify hidden narratives in social messages. Our researc

researcharxiv-cs-cl

A 25-mus/inf Event-driven Graph Neural Network Processor with Spatiotemporal Caching and Spline Convolution for Ultra-low-latency AI at the Edge

arXiv:2609.15241v1 Announce Type: new Abstract: Dynamic-vision-sensor (DVS) cameras generate events on a per-pixel basis with a mus-level temporal resolution, calling for new algorithm-hardware co-des

researcharxiv-cs-cv

A Generative AI Integrated Multimodal Framework for Low-Latency Multi-Camera Person Re-Identification

arXiv:2609.14419v1 Announce Type: cross Abstract: Person re-identification (ReID) is essential for multi-camera surveillance and tracking, yet remains difficult due to viewpoint and illumination chang

A latent dimension of Condorcet's jury theorem for multiple AI advisers

arXiv:2609.14438v1 Announce Type: cross Abstract: When the same question is asked of multiple AI advisers, as in self-consistency and LLM-as-a-judge panels, Condorcet's jury theorem predicts that addi

A property-registry contract for retrieve-or-refuse thermal-mechanical lattice search

arXiv:2609.14741v1 Announce Type: cross Abstract: Early thermal-mechanical lattice requirements are knowledge-intensive and often jointly unsatisfiable: an engineer asks for a cell that is light, stif

ActGuard: Pre-execution Action Auditing against Indirect Prompt Injection in LLM Agents

arXiv:2609.14987v1 Announce Type: cross Abstract: Large language model (LLM) agents interact with external environments through tool invocation, but tool outputs can also expose them to indirect promp

AI Psychosis: Does Conversational AI Amplify Delusion-Related Language?

arXiv:2603.19574v2 Announce Type: replace-cross Abstract: Conversational AI systems are increasingly used for personal reflection and emotional disclosure, raising concerns about their effects on vuln

Augmenting Large Audio-Language Models with Frame-Level Grounding for Fine-Grained Temporal Perception

arXiv:2609.15215v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) have substantially advanced general audio understanding, yet they remain limited in fine-grained temporal percepti

local-aiarxiv-cs-ai

Autoencoder-Based Parameter Estimation for Superposed Multi-Component Damped Sinusoidal Signals

model-releasesarxiv-cs-lg

arXiv:2604.03985v2 Announce Type: replace Abstract: Damped sinusoidal oscillations are widely observed in many physical systems, and their analysis provides access to underlying physical properties. H

AutoTailor: Automatic, User-Aligned Capability Selection and Adaptation for Web Agents

arXiv:2609.13548v1 Announce Type: new Abstract: Web agents can utilize reusable tools to reduce the cost and latency of low-level browser interaction, but automatically discovered tool collections can

C^2Nav: Compare Before You Commit for Zero-Shot Vision-and-Language Navigation

model-releasesarxiv-cs-ro

arXiv:2609.15142v1 Announce Type: new Abstract: Zero-shot vision-and-language navigation in continuous environments (VLN-CE) increasingly places foundation vision-language models (VLMs) inside the nav

Can AI systems have free will?

arXiv:2609.15407v1 Announce Type: new Abstract: While there has been much discussion of whether AI systems could function as moral agents or acquire sentience, there has been very little discussion of

Certifiably Interpretable Training of ReLU-MLPs for Boolean Tasks with Guaranteed Truth-Table Generalization

arXiv:2609.13439v1 Announce Type: cross Abstract: As compute scales, models evolve, and training algorithms advance, our ability to explain the increasingly powerful AI systems they enable is eroding.

Chance-Constrained Belief-Space Maneuver Planning for Autonomous Collision Avoidance Under Uncertainty

arXiv:2609.13428v1 Announce Type: cross Abstract: Increasing conjunction frequency in low Earth orbit places growing pressure on spacecraft operators to determine not only whether an encounter require

CIDERS: Cloud-Edge LLM Collaborative Learning via Accelerating Personalized Bilevel Optimization

arXiv:2609.15664v1 Announce Type: cross Abstract: Amid the rapid advancement of physical-world intelligence, cloud-edge collaborative large language models (LLMs) have emerged as a promising roadmap f

local-aiarxiv-cs-ai

CLEAR: Context Augmentation from Contrastive Learning of Experience via Agentic Reflection

arXiv:2604.07487v2 Announce Type: replace Abstract: Large language model agents rely on effective model context to obtain task-relevant information for decision-making. Many existing context engineeri

ClinAgent: A ReAct-Based Agent for Conversational Access to Clinical Trial Information

arXiv:2609.13860v1 Announce Type: new Abstract: Querying clinical trial registries remains a manual and error-prone process, requiring researchers to navigate large volumes of semi-structured data wit

CompCQR: Compositional Query Generation for Training-Free Conversational Search

arXiv:2609.14646v1 Announce Type: cross Abstract: Multi-turn interactions with LLMs are becoming increasingly common in information-seeking scenarios. However, user queries are often ambiguous and con

Contour-Guided Spectral Routing for Robust Real-Time Pedestrian Detection

arXiv:2609.14383v1 Announce Type: new Abstract: Real-time pedestrian detection in driving scenes is constrained by three coupled failure modes: tiny targets lose discriminative evidence, occlusion wea

safetyarxiv-cs-cv

Diagnosing Temporal Misalignment in Multichannel Time-Series Classification with Minimum Description Length

arXiv:2609.14595v1 Announce Type: cross Abstract: Multichannel time-series classification commonly assumes synchronized sensor streams, although latency, clock drift, and preprocessing can introduce r

safetyarxiv-cs-ai

Applications

DIDO: Distilling Interaction-Centric Dynamics into One-Step Denoising for World Action Models

arXiv:2609.15570v1 Announce Type: new Abstract: World Action Models (WAMs) use video generation models to predict future visual dynamics for robotic manipulation, but iterative denoising introduces ad

applicationsarxiv-cs-ro

Disparate Impact in Synthetic Data Generation

arXiv:2606.13105v2 Announce Type: replace Abstract: We revisit the fairness notion of disparate impact for synthetic data generation (SDG), that assesses whether the utility of generated records is th

safetyarxiv-cs-lg

Diversified and Perceptible Counterfactual Examples Leveraging Expert Knowledge

arXiv:2609.15609v1 Announce Type: new Abstract: CounterFactual Examples (CFEs) are a cornerstone of eXplainable Artificial Intelligence (XAI), offering local, post hoc, and model-agnostic explanations

local-aiarxiv-cs-ai

Does Attention-Guided Masking Really Help Object Discovery in Object-Centric Learning?

model-releasesarxiv-cs-cv

arXiv:2609.15187v1 Announce Type: new Abstract: Object-Centric Learning (OCL) aims to decompose images into objects without human annotations. A major family of mainstream methods uses Slot Attention

Editorial routing shapes how computational results are qualified in AI-assisted scientific writing

model-releasesarxiv-cs-cl

arXiv:2609.14288v1 Announce Type: new Abstract: Large language models increasingly analyze computational results and draft manuscripts, making reliable communication as important as correct analysis.

EEG-Xplain: Decoding Neural Black-Boxes of EEG Foundation Models

arXiv:2609.15687v1 Announce Type: new Abstract: EEG foundation models such as BIOT, LaBraM, and EEGMamba have achieved remarkable performance in neural signal decoding, but their black-box nature limi

ESG: Generating Physically Consistent Dynamic 3D Scenes from Text Descriptions

model-releasesarxiv-cs-cv

arXiv:2609.15392v1 Announce Type: cross Abstract: Recent progress in image and 3D scene generation has enabled increasingly realistic static environments, yet most methods remain confined to such stat

Estimating Uncertain Spatial Relationships in Robotics

arXiv:1304.3111v2 Announce Type: replace Abstract: In this paper, we describe a representation for spatial information, called the stochastic map, and associated procedures for building it, reading in

EventVL: Understand Event Streams via Multimodal Large Language Model

arXiv:2501.13707v3 Announce Type: replace-cross Abstract: The event-based Vision-Language Model (VLM) recently has made good progress for practical vision tasks. However, most of these works just util

safetyarxiv-cs-ai