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Approximate Speculative Decoding

arXiv:2608.03447v1 Announce Type: cross Abstract: Speculative decoding accelerates autoregressive generation by verifying a draft block with a target model in parallel. Under standard greedy verificat

ARCHead: Activation-Metric Residual Correction for Large Language Model Output Heads

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arXiv:2608.02703v1 Announce Type: new Abstract: Weight-only quantization substantially reduces the storage of large language model (LLM) transformer blocks, but practical backends often retain the fin

researcharxiv-cs-cl

5 Aug 2026

ArtECulture: Benchmarking Culture-Conditioned Visual Emotion Understanding in Multimodal Large Language Models

arXiv:2608.03358v1 Announce Type: new Abstract: Existing visual emotion understanding methods typically ignore cultural variations in emotional perception. We introduce culture-conditioned visual emot

AS-FedBridge: Pseudo-Spike Bridge Distillation for Heterogeneous ANN-SNN Federated Learning

model-releasesarxiv-cs-lg

arXiv:2608.03324v1 Announce Type: new Abstract: Federated learning enables collaborative model training across distributed edge devices while strictly preserving data privacy. To facilitate practical

Asking Questions the Right Way: A Multi-Agent Conversational System for Prompt Formulation in Complex Task Resolution

arXiv:2608.01366v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are integral to complex intellectual tasks, yet output quality remains constrained by user-provided prompts. Iter

agentsarxiv-cs-ai

Assessing speech quality metrics for evaluation of neural audio codecs under clean speech conditions

arXiv:2509.24457v1 Announce Type: cross Abstract: Objective speech-quality metrics are widely used to assess codec performance. However, for neural codecs, it is often unclear which metrics provide re

Assessing the Effect of Cross-Domain Mapping on Creativity in Humans and Large Language Models

arXiv:2603.19087v2 Announce Type: replace Abstract: Creativity is the ability to come up with novel ideas, a capacity crucial for human development and flourishing. Are large language models (LLMs) cr

Assessment of Conditional Diffusion Model for Synthetic Histopathology Image Generation

model-releasesarxiv-cs-lg

arXiv:2608.03990v1 Announce Type: new Abstract: Synthetic histopathology image generation has emerged as an approach that may address data scarcity in computational pathology, yet current evaluation m

ATFlash: Per-RoPE-Wavelength Attention Windows for Compute/Memory-Efficient LLM Inference

arXiv:2608.02947v1 Announce Type: cross Abstract: The attention score with rotary position embeddings (RoPE) decomposes exactly into a sum over its 2D-rotation frequency pairs, and each pair's wavelen

Attention is Case-Sensitive

arXiv:2608.03711v1 Announce Type: cross Abstract: In human visual perception, uppercase lettering serves as a natural salience cue that captures attention within lowercase text. In this paper, we pres

researcharxiv-cs-cl

Attribute-based Undetectable Watermarking for Generative AI Models

arXiv:2608.03174v1 Announce Type: cross Abstract: Generative AI systems increasingly produce content whose provenance is difficult to verify, motivating watermarking techniques for identifying model-g

safetyarxiv-cs-ai

Automated Visualization Code Synthesis via Multi-Path Reasoning and Feedback-Driven Optimization

arXiv:2502.11140v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have become a cornerstone for automated visualization code generation, enabling users to create charts through na

Automatic Patient-Specific Microwave Ablation Planning Accelerated by a Physics-Guided Deep Learning Model

arXiv:2608.03086v1 Announce Type: cross Abstract: Microwave ablation (MWA) is a promising minimally invasive treatment for liver tumors, but its therapeutic outcome strongly depends on patient-specifi

Autoreflection: How Agentic Strange Loops Turn Human Culture into AI Infrastructure

arXiv:2608.03800v1 Announce Type: cross Abstract: An LLM-based agent is a loop that reads itself. Agentic frameworks externalize identity, memory, and disposition into editable files. The agent loads

agentsarxiv-cs-ai

Applications

AutoSND: From Execution Evidence to Structural Policies for Automated Network Dismantling Heuristic Discovery

arXiv:2608.03653v1 Announce Type: new Abstract: Network dismantling is fundamental to analyzing the robustness and vulnerability of complex systems, yet practical heuristics must balance effectiveness

applicationsarxiv-cs-ai

Balancing Efficiency and Efficacy: Training-Free Attention-Guided Switching Between Explicit and Latent Thoughts for MLLMs

arXiv:2608.03450v1 Announce Type: cross Abstract: Reasoning in Multimodal Large Language Models (MLLMs) requires both fine-grained visual perception and rigorous logical deduction. Explicit text-based

BanglaWild: An In-the-Wild Bengali Scene Text Recognition Benchmark for OCR and Vision-Language Models

arXiv:2608.03884v1 Announce Type: cross Abstract: In-the-wild Bengali scene text recognition is largely unmeasured: existing resources target handwritten documents or constrained sign-board parsing, r

BAP-SQL: Budget-Aware Observation Planning for Agentic Text-to-SQL

arXiv:2608.02876v1 Announce Type: new Abstract: Tool-using agents do not merely consume observations: their actions determine what arrives next. In agentic text-to-SQL, a broad query can spend context

safetyarxiv-cs-ai

Bayesian Data Reweighting Improves Multimodal Retrieval for Knowledge-Based Visual Question Answering

arXiv:2608.02907v1 Announce Type: new Abstract: Multimodal retrievers are essential for knowledge-based visual question answering, where they retrieve external evidence for image-question pairs. Howev

BBOWP-Bench: Evaluating LLMs on Black-Box Optimization Word Problems

arXiv:2608.02612v1 Announce Type: new Abstract: Formulating an optimization problem strongly affects the quality of the final solution, yet good formulations usually require substantial expertise. Rec

Before Reasoning Can Fail: Pre-Evidence Procedural Failures in Agentic RAG

arXiv:2608.02011v2 Announce Type: replace Abstract: Agentic retrieval-augmented generation (RAG) systems can fail before evidence-conditioned reasoning is tested: an agent may retrieve candidate snipp

agentsarxiv-cs-ai

Behaviorally Adaptive Visual Diversion for Inclusive and Resilient Digital Assessment Delivery

arXiv:2608.03531v1 Announce Type: new Abstract: Institutions increasingly rely on browser lockdown, webcam monitoring, and behavioral analytics to secure high-stakes digital assessments, yet these mec

Applications

Benchmarking the Benchmarks: Testing the Predictive Validity of Commonsense Benchmarks

arXiv:2608.03340v1 Announce Type: new Abstract: Predicting LLM's capabilities on real-world tasks is essential, yet the extent to which performance on commonsense benchmarks predicts downstream perfor

applicationsarxiv-cs-cl

Benign interpolation and Occam's razor

arXiv:2608.03386v1 Announce Type: new Abstract: Contemporary deep learning methods generalize well even when they fit their training data perfectly, a phenomenon known as benign interpolation. This ph

Better, Stronger, Faster, and Broader: Structured All-Mask Prediction for MLLM-Based Segmentation

arXiv:2608.02791v1 Announce Type: new Abstract: MLLM-based segmentation faces a core segmentation trilemma: high segmentation performance, preserved dialogue ability, and fast inference. Embedding-pre

researcharxiv-cs-cv

Beyond Accuracy: A Multidimensional Evaluation of Statistical Reasoning in Large Language Models

arXiv:2608.03038v1 Announce Type: new Abstract: Statistical reasoning is multidimensional, yet evaluations of large language models (LLMs) typically emphasize response accuracy while overlooking how m

researcharxiv-cs-cl

Applications

Beyond Average Performance: Dynamic Instance Clustering and Specialized Algorithm Design in LLM-Assisted Evolutionary Search

arXiv:2608.03129v1 Announce Type: new Abstract: Large Language Model-assisted Evolutionary Search (LES) has emerged as a powerful paradigm for automated algorithm design. However, existing LES methods

applicationsarxiv-cs-ai

Beyond Either-Or Reasoning: Transduction and Induction as Cooperative Problem-Solving Paradigms

arXiv:2505.14744v3 Announce Type: replace-cross Abstract: Traditionally, in Programming-by-example (PBE) the goal is to synthesize a program from a small set of input-output examples. Lately, PBE has

Beyond Initialization Loss: A Systematic Study of Token Embedding Initialization Strategies for LLM Vocabulary Extension

arXiv:2608.03494v1 Announce Type: new Abstract: Vocabulary extension is an efficient way to adapt pretrained large language models (LLMs) to new languages, but the initialization of newly added token

Beyond Representational Similarity: Source-Conditioned Description-Length Gain for Generative Plagiarism Detection and Candidate Source Reranking

arXiv:2608.03859v1 Announce Type: cross Abstract: Large language models (LLMs) pose challenges to academic integrity and peer review. Yet generative plagiarism detection remains an underexplored and l

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning

arXiv:2608.03571v1 Announce Type: new Abstract: Recent works train agents by constructing large-scale multimodal environment pools. However, we find that simply increasing the number of multimodal env

agentsarxiv-cs-cv

Beyond Simulations: What 20,000 Real Conversations Reveal About Mental Health AI Safety

arXiv:2601.17003v2 Announce Type: replace-cross Abstract: Mental-health AI safety is typically evaluated with small, simulation-based benchmarks that may not reflect the linguistic and contextual dive

Local Ai

Beyond Solving: Prescriptive Probing for Neural Routing Solvers

arXiv:2602.07216v2 Announce Type: replace Abstract: Neural combinatorial optimization (NCO) trains fast heuristics for routing problems, but planners often need more than a single solve: they ask whic

local-aiarxiv-cs-lg

Beyond the Gegenbauer Paradigm: q-Orthogonal Kernels for Machine Learning

model-releasesarxiv-cs-lg

arXiv:2608.03482v1 Announce Type: new Abstract: The performance of Support Vector Machines (SVMs) critically depends on the kernel function choice, which enables implicit mapping of data into high-dim

Beyond the Hivemind: Escaping LLM Homogeneity via Meta-Persona Anchoring and Sequential Temperature Scaling

arXiv:2608.02618v1 Announce Type: new Abstract: Recent studies have identified an ``Artificial Hivemind'' effect in Large Language Models (LLMs) causing models to converge on a narrow, homogenized con

Beyond the Single Camera: Agentic Multi-View Reasoning in Sports Video Understanding

model-releasesarxiv-cs-cv

arXiv:2607.11844v2 Announce Type: replace Abstract: Recent Multimodal Large Language Models (MLLMs) achieve strong performance on single-view video understanding benchmarks. However, sports videos inv

Bi-Lipschitz Ansatz for Anti-Symmetric Functions

arXiv:2503.04263v2 Announce Type: replace Abstract: Motivated by applications to the simulation of quantum many-body systems by neural networks, researchers have suggested several models which are ant

Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language

arXiv:2608.03855v1 Announce Type: new Abstract: Transformer models have revolutionized natural language processing (NLP), and text-based molecular representations like SMILES have successfully extende

Biconvex Optimization for Smooth Minimum-Time Trajectories around Convex Obstacles

arXiv:2608.02834v1 Announce Type: new Abstract: We present a biconvex approach for minimum-time motion planning around convex obstacles that is guaranteed to converge, is anytime, and supports derivat

researcharxiv-cs-ro

Bimanual Manipulation Within an 8 GB Budget: Zero-Copy Sensing and Quantized ACT on an Entry-Level Jetson

arXiv:2608.03938v1 Announce Type: new Abstract: Bimanual manipulation policies trained with imitation learning are typically evaluated on workstation or datacenter-class GPUs, leaving the cost of depl

safetyarxiv-cs-ro

BODHI: Do LLMs Branch Out and Discover Heterogeneous Inferences?

arXiv:2608.02867v1 Announce Type: cross Abstract: Although reinforcement learning with verifiable rewards (RLVR) has improved the performance of large language models (LLMs) across a variety of reason

safetyarxiv-cs-ai

BOW: Training Language Models to Reason Over Plausible Next Words

arXiv:2506.13502v3 Announce Type: replace Abstract: Next-word prediction (NWP) trains language models against a single observed continuation, even though many contexts admit multiple plausible next wo

safetyarxiv-cs-cl

Bridging Online and Offline Handwriting via Differentiable Physical Rendering

model-releasesarxiv-cs-cv

arXiv:2608.03198v1 Announce Type: new Abstract: Realistic handwritten text generation plays an important role in numerous applications, such as font design, biometric authentication, and robotic calli

Bridging Prediction and Attribution: Identifying Forward and Backward Causal Influence Ranges Using Assimilative Causal Inference

arXiv:2510.21889v2 Announce Type: replace-cross Abstract: Causal inference identifies cause-and-effect relationships between variables. While traditional approaches rely on data to reveal causal links

safetyarxiv-cs-lg

BulkPR-Bench: Benchmarking Queue-Level Governance of Interacting Pull Requests

arXiv:2608.02685v1 Announce Type: cross Abstract: Coding-agent benchmarks increasingly cover long-horizon, end-to-end, and interactive development, but typically retain one requested outcome or a fixe

CADET: Physics-Grounded Causal Auditing and Training-Free Deconfounding of End-to-End Driving Planners

arXiv:2606.14438v3 Announce Type: replace-cross Abstract: End-to-end (E2E) autonomous-driving planners trained by imitation are prone to statistical shortcuts: they associate scene elements that merel

Calibrating Semantic Uncertainty from Observable Language-Model Probabilities

arXiv:2607.17447v2 Announce Type: replace-cross Abstract: As generative artificial intelligence enters scientific and professional work, its uncertainty must be defined on the states that matter for i

researcharxiv-cs-cl

Can Large Language Models Recover Semantic Optimization Opportunities That Compilers Miss?

arXiv:2608.03983v1 Announce Type: cross Abstract: Optimizing compilers miss profitable transformations when their enabling semantics are absent from the analyzed program representation. We ask whether