Search: “framework”

Where Identity Lives: Localized, Retain-Free Identity Unlearning in Multimodal Large Language Models

arXiv:2608.30649v1 Announce Type: new Abstract: Removing a specific individual's information from multimodal large language models (MLLMs) is often needed after deployment, but existing methods rely o

local-aiarxiv-cs-cl

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Who Flips? Self- and Cross-Model Counterarguments Reveal Answer Instability in LLMs

DGX agent

arXiv:2606.16011v2 Announce Type: replace Abstract: Standard accuracy benchmarks evaluate whether large language models (LLMs) reach correct answers. However, they do not test whether models maintain

model-releasesarxiv-cs-cl

1 Sept 2026

Wide Learning: Learning to Reach Evidence

arXiv:2608.29608v1 Announce Type: cross Abstract: Machine learning is usually evaluated after an evidence interface has been fixed. A dataset, sensor suite, query language, action set, or experimental

researcharxiv-cs-ai

WorldCupArena: Fine-Grained Evaluation of Language Models and Deep-Research Agents on Football Forecasting

model-releasesarxiv-cs-ai

arXiv:2607.18084v2 Announce Type: replace Abstract: Predicting a football match before kickoff requires more than knowing past results: a model must use changing information and make a clear predictio

Wrong Prediction, Right Answer: Recovering Evidence from Collapsed LLM Sequence Scores

model-releasesarxiv-cs-ai

arXiv:2608.31068v1 Announce Type: new Abstract: When a large language model fails a reasoning task, it is often assumed to lack the underlying capability. However, this conflates a genuine absence of

A comprehensive and trustworthy benchmark of AI methods for change detection in Earth observation

arXiv:2608.28247v1 Announce Type: new Abstract: Change detection in Earth observation (EO) is critical for monitoring land surface transformations, yet recent research in the field is constrained by i

Acquire, Repair, Preserve: A Diagnosis-Guided Post-Training Recipe for Small-Model Dialogue Game Agents

arXiv:2608.28458v1 Announce Type: new Abstract: Interactive dialogue games test a capability that static benchmarks largely leave implicit: a model must carry state across turns, interpret feedback, a

local-aiarxiv-cs-cl

Ada-TokenCom: Rate-Adaptive Token Communications via Large-Model-Driven Token Compression and Generation

arXiv:2608.28086v1 Announce Type: cross Abstract: Token Communications (TokenCom) has recently emerged as a new paradigm in which tokens serve as unified units for communication and computation, enabl

researcharxiv-cs-cv

Ampere: Communication-Efficient and High-Accuracy Split Federated Learning

arXiv:2507.07130v2 Announce Type: replace-cross Abstract: A Federated Learning (FL) system collaboratively trains neural networks across devices and a server but is limited by significant on-device co

local-aiarxiv-cs-lg

Are These Modules Worth Their Cost? A Paradigm-Level Accuracy-Cost Analysis of In-context Learning Text-to-SQL

arXiv:2608.28432v1 Announce Type: new Abstract: Recent advances in in-context learning (ICL) text-to-SQL have substantially improved execution accuracy on public benchmarks by assembling increasingly

Below the Noise Floor: Bimodal Seed Collapse and Distinct Failure Modes in Small-Model Knowledge Distillation

arXiv:2608.27729v1 Announce Type: new Abstract: Function routing -- selecting the correct API call from a fixed catalog given a natural-language request -- is a deployment problem where small students

Beyond Pixels: Visual Metaphor Transfer via Schema-Driven Agentic Reasoning

arXiv:2602.01335v2 Announce Type: replace Abstract: A visual metaphor constitutes a high-order form of human creativity, employing cross-domain semantic fusion to transform abstract concepts into impa

safetyarxiv-cs-cv

CamoDocs: A Poisoning Attack Against Retrieval-Augmented Language Models Using Camouflaged Documents

arXiv:2608.28389v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) augments LLMs with external documents, but public or user-editable sources expose RAG systems to data poisoning:

CAVE-NAV: VLM-Based Autonomous 3D Navigation in Underwater Cave Environments

arXiv:2608.27793v1 Announce Type: new Abstract: Autonomous navigation in underwater cave environments is essential for search-and-rescue operations, scientific exploration, and emergency egress. Tradi

local-aiarxiv-cs-ro

Climate Physics Dynamic Matching

arXiv:2608.26907v1 Announce Type: cross Abstract: Deep generative models such as flow matching and diffusion models have shown potential for learning complex dynamical systems, but typically act as bl

Coordinated Motion Planning for Multi-Arm Systems via Iterative LQ Games

arXiv:2608.27726v1 Announce Type: new Abstract: Multi-agent motion planning for high-degree-of-freedom robotics manipulators in shared workspaces remains a fundamental yet challenging problem. Central

safetyarxiv-cs-ro

Cut-ViT: Task-Specific Model Pruning via Gram Anchoring Subspace Consistency

arXiv:2608.28205v1 Announce Type: new Abstract: Pruning visual foundation models has attracted considerable attention. However, existing methods focus on rigid point-to-point token alignment on a sing

safetyarxiv-cs-cv

Differentiable Dynamics and Fast Simulation of Continuous Elastic Robotic Fish

arXiv:2509.16145v3 Announce Type: replace Abstract: Body flexibility plays a critical role in fish-like swimming, as the spatial distribution of stiffness governs body deformation, hydrodynamic loadin

researcharxiv-cs-ro

Diffusion Distillation for Efficient Weather Ensembles

arXiv:2608.27728v1 Announce Type: new Abstract: Diffusion models generate skillful weather ensembles but require costly iterative sampling. We introduce a supervised energy-distance distillation metho

Effectiveness of IoT and Deep Learning for Detection and Severity Assessment of Postelectrotermes militaris in Tea Plantations

arXiv:2608.27480v1 Announce Type: cross Abstract: Tea plantations are vulnerable to Postelectrotermes militaris, commonly known as the Upcountry Live Wood Termite (ULWT), which can cause substantial d

Empowering Local Agriculture: A Deep Learning-Powered Web System for Identifying Bangladeshi Mango Varieties

arXiv:2608.28161v1 Announce Type: new Abstract: Mango variety identification in Bangladesh is challenging because closely related cultivars can have similar visual characteristics and images are often

local-aiarxiv-cs-cv

GRACE:Gradient-guided Coreset Selection for LLM Unlearning

arXiv:2608.28361v1 Announce Type: cross Abstract: Machine Unlearning methods for Large Language Models typically assume pre-specified forget and retain sets. In realistic settings, however, requests m

Improved off-policy training of diffusion samplers

model-releasesarxiv-cs-lg

arXiv:2402.05098v5 Announce Type: replace Abstract: We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function. We benchmark s

InfoMamba: An Attention-Free Hybrid Mamba-Transformer Model

arXiv:2603.18031v2 Announce Type: replace Abstract: Balancing fine-grained local modeling with long-range dependency capture under computational constraints remains a central challenge in sequence mod

Learning a Size-Weight Frontier for Synthetic-Augmented Inference

arXiv:2608.28576v1 Announce Type: cross Abstract: Synthetic data can improve statistical inference when real data are scarce, but naively treating synthetic samples as real data can introduce bias and

Learning the Target Priors Before Image Translation: A Decoupled Training Paradigm for Cross-Modal Image Translation in Remote Sensing

arXiv:2608.28517v1 Announce Type: new Abstract: Cross-modal image translation in remote sensing must preserve source-observed content while matching the target-domain distribution. Existing methods jo

Load-Bearing Context: The Question Damage Score for Evaluating Context Reliance in Linguistic Reasoning

arXiv:2608.27756v1 Announce Type: new Abstract: Determining whether large language models derive answers from context or prior knowledge remains a fundamental challenge. Self-contained linguistic olym

Meta-Prompt Optimization for LLM-Based Sequential Decision Making

arXiv:2502.00728v2 Announce Type: replace Abstract: Large language models (LLMs) have recently been employed as agents to solve sequential decision-making tasks such as Bayesian optimization and multi

Hardware

Not to Break, but to Attest: Adversarial Probes for Privacy-Preserving LLM Verification

arXiv:2608.27954v1 Announce Type: cross Abstract: Post-deployment changes to large language models can alter behavior while leaving routine outputs largely unchanged, creating a challenge for AI gover

hardwarearxiv-cs-lg

One Model for All: Universal Pre-training for EEG based Emotion Recognition across Heterogeneous Datasets and Paradigms

model-releasesarxiv-cs-lg

arXiv:2511.08444v2 Announce Type: replace Abstract: EEG-based emotion recognition is hampered by profound dataset heterogeneity (channel/subject variability), hindering generalizable models. Existing

Plan Along the Way: Event-Triggered Foundation-Model Planning for TAMP Execution in Partially Observable Manipulation

arXiv:2608.28075v1 Announce Type: new Abstract: Manipulation in partially observable environments requires planning under incomplete scene information. In such settings, an initially valid plan may ex

researcharxiv-cs-ro

Probabilistic Multi-Robot Gas Source Localization with Uncalibrated Sensors: A Distributed Estimation Approach

model-releasesarxiv-cs-ro

arXiv:2608.28214v1 Announce Type: new Abstract: Estimating environmental states with multi-robot systems becomes particularly challenging when robots are equipped with uncalibrated and therefore heter

TokenPilot: Cache-Efficient Context Management for LLM Agents

arXiv:2606.17016v2 Announce Type: replace Abstract: As LLM agents are deployed in long-horizon sessions, context accumulation drives up inference costs. Existing approaches utilize text pruning or dyn

Towards a mathematical theory of superposition

arXiv:2608.27540v1 Announce Type: cross Abstract: We develop a mathematical theory of superposition in neural networks using tools from frame theory and compressed sensing. In our model, a sparse bina

Trajectory-Level Speculative Decoding for Diffusion Language Models

arXiv:2608.27514v1 Announce Type: new Abstract: Diffusion-based language models (dLLMs) enable parallel token generation through iterative denoising, but existing decoding strategies collapse to singl

When Can Conditional Flow Matching Replace Pointwise Negative Log-Likelihood?

arXiv:2608.28010v1 Announce Type: new Abstract: Flow matching enables likelihood-free training, yet alignment methods increasingly reuse conditional flow matching (CFM) losses as endpoint negative log

A Flexible Empirical Bayes Approach to Generalized Linear Models, with Applications to Sparse Logistic Regression

arXiv:2601.21217v2 Announce Type: replace-cross Abstract: We introduce a flexible empirical Bayes approach for fitting Bayesian generalized linear models. Specifically, we adopt a novel mean-field var

A Safety-Gated Multimodal AI Backend for Mental-Health Support: Hierarchical State Representation, Conservative Risk Fusion, and Controlled Generation in Anian

arXiv:2608.26162v1 Announce Type: new Abstract: Safety-critical mental-health support systems must distinguish when supportive conversation is appropriate from when free-form generation should be bloc

local-aiarxiv-cs-ai

Accelerating Scientific Research with Gemini in the Real-World

model-releasesarxiv-cs-ai

arXiv:2608.26701v1 Announce Type: new Abstract: We present an extension and comprehensive real-world validation of Co-Scientist, a Gemini-based multi-agent system designed to accelerate end-to-end sci

Active Preference Learning over Latent Preference Archetypes for Many-Objective Bayesian Optimization

arXiv:2603.28410v2 Announce Type: replace Abstract: Preference-based many-objective Bayesian optimization typically assumes that all pairwise comparisons arise from a single latent utility function, d

Agents

Agent Mesh: Reliability Primitives for Non-Idempotent Agent Delegation - Identity Adequacy and Evidence Adequacy

arXiv:2608.26225v1 Announce Type: new Abstract: Autonomous agents increasingly perform bounded software tasks under an orchestrator that retries, resumes, and budgets them. The machinery such orchestr

agentsarxiv-cs-ai

Agents Don't Paginate: First-Chunk Selection for LLM Tool Responses

arXiv:2608.26130v1 Announce Type: new Abstract: Coding agents built on large language models (LLMs), such as Claude Code, Cursor, OpenAI Codex, GitHub Copilot, and Aider, receive tool responses that r

Algebraic Multigrid Acceleration for Efficient Label Spreading

arXiv:2608.26309v1 Announce Type: new Abstract: Modern machine learning models rely on large amounts of labeled data. However, manual annotation of large-scale datasets is expensive and time-consuming

Ancient-Bench: A Comprehensive Multi-millennial, Multi-medium, and Multi-script Benchmark for Ancient Chinese Artifact Text Recognition

arXiv:2608.27169v1 Announce Type: new Abstract: Ancient Chinese artifact text recognition is fundamental to heritage digitization, and benchmarks for ancient texts are essential for evaluating current

Are Large Language Models Effective Knowledge Graph Constructors?

arXiv:2510.11297v2 Announce Type: replace Abstract: Knowledge graphs (KGs) are widely used in knowledge-intensive applications, yet it remains unclear how effectively current large language models (LL

Artificial Intelligence Models Can Predict and Collaboratively Modulate Human Memory Search

arXiv:2608.26152v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit unprecedented natural language generation and many text-based problem-solving capabilities. Indeed, in many langu

researcharxiv-cs-ai

Bayesian methods and Markov chain Monte Carlo algorithms for curve reconstruction and point cloud data analysis

arXiv:2608.26490v1 Announce Type: new Abstract: Point-cloud data routinely captured by modern imaging and sensor technologies provide detailed geometric descriptions of objects and environments, but t

local-aiarxiv-cs-lg

Beyond Execution: Auditing Experimental Fidelity in LLM-Driven Scientific Research

arXiv:2608.26753v1 Announce Type: cross Abstract: LLM agents used for scientific experimentation must do more than generate executable code: they must implement the reference method faithfully, design

researcharxiv-cs-ai