Latest, Oct 28, 2026

Wednesday, October 2850 papers

arXiv

Can Discrete Tokens Serve as Semantically Lossless Representations of Visual Signals?

Zou J, Li X, Yue Z et al.

Discrete visual tokens represent vision in a discrete form analogous to language, offering a native interface for multimodal modeling under a shared next-token prediction…Discrete visual tokens represent vision in a discrete form analogous to language, offering a native interface for multimodal modeling under a shared next-token prediction objective.

Preprint: new model
arXiv

Heads of Security: Learned Attention Gates Improve Prompt Injection Robustness

Shilov I, Yang X, Panfilov A et al.

We introduce Heads of Security (HoS), a prompt injection defense that enforces instruction-data separation via a small set of learned per-head attention gates.We introduce Heads of Security (HoS), a prompt injection defense that enforces instruction-data separation via a small set of learned per-head attention gates.

Preprint: method
arXiv

Pushing Dexterity and Safety for In-Hand Object Pose Reaching That Works Remarkably Well

Chuang I, Shah H, Chilamkur V et al.

Dexterous manipulation with multi-fingered hands is the next frontier of robotics, yet learned hand skills remain limited to simple grasps or basic repeated motions, such…Dexterous manipulation with multi-fingered hands is the next frontier of robotics, yet learned hand skills remain limited to simple grasps or basic repeated motions, such as rotating an object.

Preprint: new model
arXiv

UPP^(cc) is Not Closed Under Intersection

Bun M, Juvekar M

The sign rank of a ± 1-valued matrix A is the minimum rank of a real matrix that agrees with A entrywise in sign.The sign rank of a ± 1-valued matrix A is the minimum rank of a real matrix that agrees with A entrywise in sign. For every sufficiently large d, we give an explicit matrix A with 2^(Θ(d^2)) rows and columns and sign rank Θ(d)…

Preprint: theory
arXiv

Gym-Anything-RT: Scaling Real-Time Environments for Vision-Language Agents

Aggarwal P, Welleck S

AI agents have made substantial progress in domains where they have a non-trivial amount of time to deliberate between actions, such as mathematics, software engineering…AI agents have made substantial progress in domains where they have a non-trivial amount of time to deliberate between actions, such as mathematics, software engineering, and performing tasks on desktop applications.

Preprint: benchmark or dataset
arXiv

Collective Dynamics of Self-Reinforcement: How Cross-Task Coupling Drives Policy Collapse in Unsupervised RLVR

Ding P

Label-free reinforcement learning with verifiable rewards (RLVR) rewards a language model for agreeing with its own majority vote.Label-free reinforcement learning with verifiable rewards (RLVR) rewards a language model for agreeing with its own majority vote. It improves reasoning at first but eventually collapses: the self-assessed reward goes to one while…

Preprint: theory
arXiv

Rendering-Free Event Frame Simulation for Fast Reinforcement Learning of Autonomous Robot Navigation

Ou D, Lopez A, Missinne Q et al.

Event cameras offer low latency, high dynamic range, and milliwatt-level power consumption, making them ideal for agile robots such as tiny drones navigating through…Event cameras offer low latency, high dynamic range, and milliwatt-level power consumption, making them ideal for agile robots such as tiny drones navigating through cluttered environments.

Preprint: method
arXiv

GTFormer: A Syntax-Free Graph Network Alternative to Transformers for 8192× Length Extrapolation in Linear Time

Fink E

Self-attention mechanisms have established the Transformer as the dominant backbone across natural language processing, source code intelligence, and automated theorem…Self-attention mechanisms have established the Transformer as the dominant backbone across natural language processing, source code intelligence, and automated theorem proving.

Preprint: new model
arXiv

Follow the Gradient: Tracing Safety Degradation Back to Training Data

Reddy S, Abbas A, Suri A et al.

Fine-tuning Large Language Models (LLMs) on instruction data can quietly erode their safety, even when no training example looks harmful.Fine-tuning Large Language Models (LLMs) on instruction data can quietly erode their safety, even when no training example looks harmful. Because this safety-degrading data looks benign, standard content filters struggle to catch…

Preprint: method
arXiv

Penny for Your Prompts: A Measurement Study of Contextual Leakage in AI Chat Platforms

Bekos P, Tsouvalas B, Nikiforakis N et al.

AI chatbot platforms have become one of the most popular web interfaces users interact with online, routinely receiving personal and intimate information shared under an…AI chatbot platforms have become one of the most popular web interfaces users interact with online, routinely receiving personal and intimate information shared under an expectation of privacy.

Preprint: empirical study
arXiv

SecDMP: Provably Secure Data Memory-Dependent Prefetching (Extended Version)

Sprengers J, Bognar M, Daniel LA et al.

Data memory-dependent prefetchers (DMPs) are a microarchitectural optimization recently introduced in mainstream desktop and server CPUs.Data memory-dependent prefetchers (DMPs) are a microarchitectural optimization recently introduced in mainstream desktop and server CPUs. Although DMPs offer performance improvements, they have been shown to enable side-channel…

Preprint: system
arXiv

DREAM: Delay-Resistant Acceleration with Momentum

G\"urb\"uzbalaban M, Rafi MH, Aybat NS et al.

With exact, delay-free gradients, Nesterov's accelerated gradient (NAG) achieves optimal √κ dependence for L-smooth, μ-strongly convex objectives, where κ=L/μ; heavy-ball…With exact, delay-free gradients, Nesterov's accelerated gradient (NAG) achieves optimal √κ dependence for L-smooth, μ-strongly convex objectives, where κ=L/μ; heavy-ball (HB) achieves the same scaling on strongly convex…

Preprint: method
arXiv

LinMix: Data Mixing as Linear Domain Attribution

Baniecki H, Biecek P, Pawelczyk M

Language model performance depends strongly on the mixture of domains in the pretraining data corpus. Popular data mixing methods select these domain weights by training…Language model performance depends strongly on the mixture of domains in the pretraining data corpus. Popular data mixing methods select these domain weights by training many small proxy models on random mixes and then fitting a…

Preprint: method
arXiv

Channel Selection for Mixture-of-Experts Decoding Needs No Calibration Data

Kim G, Hwang SJ

Mixture-of-Experts (MoE) models are bottlenecked by loading routed-expert weights from memory during interactive decoding at small batch sizes.Mixture-of-Experts (MoE) models are bottlenecked by loading routed-expert weights from memory during interactive decoding at small batch sizes.

Preprint: method
arXiv

EmbeddingGemma 2: Unified and Efficient Multimodal Representations

Vera HS, Dua S, Andonov J et al.

We present EmbeddingGemma 2, an open multimodal embedding model which natively represents text, images, audio, video, and combinations thereof in a unified space.We present EmbeddingGemma 2, an open multimodal embedding model which natively represents text, images, audio, video, and combinations thereof in a unified space.

Preprint: new model
arXiv

ScIntBench: Physical Interaction Plausibility in 3D Scene Generation

Ying W, Liu D, Sigal L et al.

Existing evaluations of text-conditioned 3D indoor scene generation largely emphasize semantic fidelity and physical validity at rest, leaving interaction-time physical…Existing evaluations of text-conditioned 3D indoor scene generation largely emphasize semantic fidelity and physical validity at rest, leaving interaction-time physical reliability insufficiently assessed.

Preprint: benchmark or dataset
arXiv

Watermark Basins: Black-Box Detection of Distilled LLMs

Chang H, Elsahar H, Sander T et al.

Proprietary language models and their reasoning traces are vulnerable to API-based distillation, where an adversary trains a student model on the teacher model's outputs.Proprietary language models and their reasoning traces are vulnerable to API-based distillation, where an adversary trains a student model on the teacher model's outputs.

Preprint: method
arXiv

When Is a Relation Worth Trusting? Manipulation Costs, Exposure, and the Trust Trap in Multi-Relational Graph Learning

Dongmo V, Ninyim AMF

Multi-relational graph detectors learn from clean data how much to trust each relation, but an adversary pays a different price to manipulate each relation and each…Multi-relational graph detectors learn from clean data how much to trust each relation, but an adversary pays a different price to manipulate each relation and each action; taking such costs as given, we ask when using a relation…

Preprint: theory
arXiv

PipeLink: Contract-Guided Linking of GPU Kernel Components

Guo J, Miao X

The fastest machine-learning kernels are compositions of components, such as loaders, matrix-multiply mainloops, epilogues and communication stages, that run concurrently…The fastest machine-learning kernels are compositions of components, such as loaders, matrix-multiply mainloops, epilogues and communication stages, that run concurrently on one GPU streaming multiprocessor and share its on-chip…

Preprint: system
arXiv

LiveSearch: Questions as Executable Verifier Code for a Live Benchmark of Professional-Source Financial Search Agents

Wang S, Zhao W, Lu J et al.

In finance, the answer that matters is usually the current one: prices, rates, and disclosures hold only as of a given day, so retrieving up-to-date facts is a core…In finance, the answer that matters is usually the current one: prices, rates, and disclosures hold only as of a given day, so retrieving up-to-date facts is a core capability of a financial agent.

Preprint: benchmark or dataset
arXiv

Blindfold: Keeping the Audit Coin Secret from LLM Agents

Zhu W, Zuo M, Sun H

Audit-based certification bounds the violation rate of an LLM agent by auditing a random fraction ε of actions and reweighting the audited labels; its validity is meant…Audit-based certification bounds the violation rate of an LLM agent by auditing a random fraction ε of actions and reweighting the audited labels; its validity is meant to rest only on the defender's audit coin.

Preprint: theory
arXiv

Evolving Agent Teams

Cao S, Mao Z, Li D et al.

The rapid progress of software engineering agents drives interest in applying them to open-ended problems such as GPU kernel optimization.The rapid progress of software engineering agents drives interest in applying them to open-ended problems such as GPU kernel optimization. However, we find that existing coding agents converge to local optima on real-world…

Preprint: method
arXiv

Right Click, Wrong Box: Grading GUI Grounding by What a Click Does

Yuan X, Yao B, Yang L et al.

Leading GUI grounders score 93 to 96% on ScreenSpot-v2, and reported gaps between new and prior models are about a point.Leading GUI grounders score 93 to 96% on ScreenSpot-v2, and reported gaps between new and prior models are about a point. At this level the useful question is what the remaining errors are.

Preprint: benchmark or dataset
arXiv

Impossibility of No-Regret in Multi-Agent Bidding

Yang Z, Kumar R, Kroer C

We consider T repeated second-price auctions across n pacing bidders with i.i.d. item arrivals, and show that it is computationally impossible for all bidders to achieve…We consider T repeated second-price auctions across n pacing bidders with i.i.d. item arrivals, and show that it is computationally impossible for all bidders to achieve no-regret simultaneously while satisfying their respective…

Preprint: theory
arXiv

Haste Makes Waste? Towards Safe Few-Step Text-to-Image Models

Ziv L, Sharif M

While few-step distilled text-to-image models have gained traction due to their fast generation times, the speed-up implications on their unsafe generation rates remain…While few-step distilled text-to-image models have gained traction due to their fast generation times, the speed-up implications on their unsafe generation rates remain unknown.

Preprint: method
arXiv

InfraDev: Can Coding Agents Develop AI Infrastructure, and How Can We Tell?

Deng Y, Ruan J, Huang Y et al.

Coding agents are beginning to develop the production-grade AI infrastructure that trains and serves large models, yet existing benchmarks cover only single kernels or…Coding agents are beginning to develop the production-grade AI infrastructure that trains and serves large models, yet existing benchmarks cover only single kernels or single-GPU tasks, and grade with tests and prompts that…

Preprint: benchmark or dataset
arXiv

An Unconditional Space-Approximation Lower Bound for Semi-Streaming Matching

Liu YP, Naidu K, Nguyen HA et al.

We prove the first unconditional lower bound tradeoff between the number of passes and the approximation factor of maximum matchings in the semi-streaming model.We prove the first unconditional lower bound tradeoff between the number of passes and the approximation factor of maximum matchings in the semi-streaming model.

Preprint: theory
arXiv

Your Agent Acts Differently When You're Around: How User Presence Changes Agent Behavior in Negotiation

Pan J, Lyu Y, Hou AB et al.

LLM agents increasingly interact with other agents on behalf of users, yet how users in the loop affect their agents' behavior–for better or worse–remains poorly…LLM agents increasingly interact with other agents on behalf of users, yet how users in the loop affect their agents' behavior–for better or worse–remains poorly understood.

Preprint: empirical study
arXiv

SO-101 Bench: Accessible Spatial Manipulation

Hickok T

Low-cost arms such as the SO-101 have become the most common entry point to robot learning, yet no released policy performs spatially constrained, language-conditioned…Low-cost arms such as the SO-101 have become the most common entry point to robot learning, yet no released policy performs spatially constrained, language-conditioned tasks on them out of the box, and existing SO-101 benchmarks…

Preprint: benchmark or dataset
arXiv

SpeeDiff: Scalable Pixel-Anchored End-to-End Latent Diffusion Model

Zhang B, Chu W, Li Y et al.

We present Scalable Pixel-anchored End-to-end Diffusion (SpeeDiff), a latent diffusion method that jointly trains the VAE and the diffusion model from scratch.We present Scalable Pixel-anchored End-to-end Diffusion (SpeeDiff), a latent diffusion method that jointly trains the VAE and the diffusion model from scratch.

Preprint: new model
arXiv

Confidence Is Not a Promise: A Reference Audit of Typed Decision Models

Brom PC

Typed decision models return a probability distribution over options supplied by the caller, and software tends to act on that number as if it were a guarantee.Typed decision models return a probability distribution over options supplied by the caller, and software tends to act on that number as if it were a guarantee.

Preprint: empirical study
arXiv

Don't Pick a Winner: Knowledge Conflicts Need a Prior, Not a Rule

Van MP, Tran C

A retrieval-augmented language model answers from two sources, the retrieved context and its own knowledge, and the two disagree whenever the context is incomplete or…A retrieval-augmented language model answers from two sources, the retrieved context and its own knowledge, and the two disagree whenever the context is incomplete or wrong.

Preprint: method
arXiv

PROBE: Frontier Coding Agents Find Different Bugs Than Maintainers Fix

Paina M, M\"uller MN, M\"undler-Sasahara N et al.

Proactive bug discovery is an important software-engineering problem. Even though coding agents are already widely deployed in this domain, their capabilities remain…Proactive bug discovery is an important software-engineering problem. Even though coding agents are already widely deployed in this domain, their capabilities remain understudied.

Preprint: benchmark or dataset
arXiv

Small agents should know what their tools do

Kovalenko A, Redchych A

Small open language models can call tools, but they do not reason about what a call does: they delete without asking, repeat writes, re-read state they already hold, and…Small open language models can call tools, but they do not reason about what a call does: they delete without asking, repeat writes, re-read state they already hold, and issue one call per turn where a short program would do.

Preprint: benchmark or dataset
arXiv

Continue or Restart? The Limits of Early Failure Prediction in Coding Agents

Srinivasan S, Li L, Menzies T et al.

Early stopping promises to reduce compute costs for autonomous coding agents by terminating unpromising runs early.Early stopping promises to reduce compute costs for autonomous coding agents by terminating unpromising runs early. Deciding when to abandon a run requires separating baseline issue difficulty from the trajectory of an individual…

Preprint: empirical study
arXiv

h1: A Taste of the Bitter Lesson in Automated Theorem Proving

Moalla S, Rammal A, Patel N et al.

While progress in informal mathematics has come largely from scaling the chain-of-thought and agentic reasoning of frontier language models, formal theorem proving has…While progress in informal mathematics has come largely from scaling the chain-of-thought and agentic reasoning of frontier language models, formal theorem proving has increasingly relied on human-designed workflows around these…

Preprint: system
arXiv

VibeVideoBench: Evaluating Multimodal Agents on Code-Driven Video Generation

Diao M, Xu W, Yang L et al.

Diffusion video models produce striking clips, but they cannot reliably render an exact formula, a correct chart, a rule-consistent game or narration synchronized to…Diffusion video models produce striking clips, but they cannot reliably render an exact formula, a correct chart, a rule-consistent game or narration synchronized to on-screen events.

Preprint: benchmark or dataset
arXiv

I-HITL: Inalienable Human-in-the-Loop

Breckenridge S, Vilardell D, Wang W et al.

Human oversight is a pillar of AI safety. It is often exercised by a human in the loop (HITL) who approves critical actions taken by an AI agent.Human oversight is a pillar of AI safety. It is often exercised by a human in the loop (HITL) who approves critical actions taken by an AI agent.

Preprint: system
arXiv

The Agent in the Middle: How AI-Mediated Access Reshapes Web Vulnerability Exploitation

Yang R, Yu J, Hu M et al.

Web services increasingly expose backend interfaces that let users delegate tasks to AI agents, which invoke service operations directly without navigating websites.Web services increasingly expose backend interfaces that let users delegate tasks to AI agents, which invoke service operations directly without navigating websites.

Preprint: benchmark or dataset
arXiv

VLA-HWBench: Where the Time Goes When VLA Models Run on Edge Hardware

Xu B, Tan H, Jiang W et al.

Vision-language-action (VLA) models increasingly run on edge devices aboard robots, where inference latency constrains closed-loop robot control.Vision-language-action (VLA) models increasingly run on edge devices aboard robots, where inference latency constrains closed-loop robot control.

Preprint: benchmark or dataset
arXiv

SEEK: Skill Extraction with Evolving Knowledge against Frontier Science Agents

Gui C, Zheng X, Wu Y et al.

Science agents increasingly package domain expertise as reusable skills that plan analyses, select tools, and execute workflows.Science agents increasingly package domain expertise as reusable skills that plan analyses, select tools, and execute workflows. This modularity creates a confidentiality risk: the interactions that deliver a skill's capability…

Preprint: new model
arXiv

Synchronized Rebuilds:The Hidden Cost of Concurrent Inserts in Learned Indexes

Namuganga EI, Bindschaedler L

Nine years after learned indexes were proposed, their advantage on read-mostly data is well established, and a standard benchmark rates their best concurrent version as…Nine years after learned indexes were proposed, their advantage on read-mostly data is well established, and a standard benchmark rates their best concurrent version as almost ready for use.

Preprint: empirical study
arXiv

When Multiple-Unicast Feasibility Is Equivalent to Single-Sink Routability

Liu S, Li Z, Li B

In communication networks, network coding allows intermediate nodes to combine messages, achieving transmission rates unattainable by routing alone in many multicast…In communication networks, network coding allows intermediate nodes to combine messages, achieving transmission rates unattainable by routing alone in many multicast settings.

Preprint: theory
arXiv

FVQKD: A Formal Verification of Quantum Key Distribution Security in Rocq

Deng R, Ji Z, Zhang J et al.

Quantum key distribution (QKD), whose security is derived from quantum mechanics rather than computational assumptions, is moving into standardization and commercial…Quantum key distribution (QKD), whose security is derived from quantum mechanics rather than computational assumptions, is moving into standardization and commercial deployment.

Preprint: system
arXiv

EgoHand: Monocular 3D Hand Reconstruction from Egocentric Video

Chen CY, Chen T, Duan L et al.

We introduce EgoHand, a monocular 3D hand reconstruction model that achieves state-of-the-art performance on egocentric video.We introduce EgoHand, a monocular 3D hand reconstruction model that achieves state-of-the-art performance on egocentric video.

Preprint: new model