Search: “context”

SIPTraj: Map-Free End-to-End Trajectory Prediction via Physics-Guided Scene Interaction

model-releasesarxiv-cs-ro

arXiv:2608.00779v1 Announce Type: new Abstract: Trajectory prediction of surrounding agents is a prerequisite for safe planning and decision making in autonomous driving. Without high-definition (HD)

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Model Releases

SoM-1K: A Thousand-Problem Benchmark Dataset for Strength of Materials

DGX agent

arXiv:2509.21079v2 Announce Type: replace Abstract: Foundation models have shown remarkable capabilities in various domains, but their performance on complex, multimodal engineering problems remains l

model-releasesarxiv-cs-cl

4 Aug 2026

StreamSplat: Streaming Feed-Forward 3D Gaussian Splatting

arXiv:2608.01659v1 Announce Type: new Abstract: Feed-forward 3D Gaussian Splatting enables efficient novel-view synthesis without per-scene optimization, but most existing methods assume a fixed set o

researcharxiv-cs-cv

TIDES: A Longitudinal Bilingual Dataset for Modeling Multi-Party Social Dynamics

arXiv:2608.01724v1 Announce Type: new Abstract: Group conversations are fundamental to human collaboration, yet standard large language models (LLMs) still struggle with the complexities of multi-part

applicationsarxiv-cs-cl

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability

arXiv:2608.02238v1 Announce Type: cross Abstract: Ensuring trust in AI systems is essential for the safe and ethical integration of machine learning systems into high-stakes domains such as digital he

safetyarxiv-cs-lg

UEmbed: Unified Sparse and Dense Multimodal Embeddings

arXiv:2608.02583v1 Announce Type: cross Abstract: Sparse retrieval underpins modern search systems, from web search to retrieval-augmented generation. Existing work has introduced Learned Sparse Retri

agentsarxiv-cs-cl

V-Mem: Modality-Routed Retrieval for Long-Term Multimodal Agentic Memory

arXiv:2608.01543v1 Announce Type: cross Abstract: Interaction between users and LLM agents is increasingly multimodal: conversations interleave text with images, and a later question may target either

agentsarxiv-cs-cl

Visualising Information Flow in Word Embeddings with Diffusion Tensor Imaging

arXiv:2601.05713v2 Announce Type: replace Abstract: Understanding how large language models (LLMs) represent natural language is a central challenge in natural language processing (NLP) research. Many

researcharxiv-cs-cl

A Unified Benchmark of Deep Learning Models for Multi-task 3D Brain Tumor Segmentation from Magnetic Resonance Imaging

arXiv:2607.28858v1 Announce Type: cross Abstract: Automatic brain tumor segmentation from magnetic resonance imaging (MRI) has become a fundamental task in computer-assisted diagnosis, treatment plann

Local Ai

Best Friends, Not Forever: Evaluating Long-Horizon Persona Collapse and Behavioral Drift in AI Companions

arXiv:2607.28818v1 Announce Type: new Abstract: As AI companions increasingly mediate repeated social interaction, users may rely on a stable role and shared history, yet locally acceptable replies do

local-aiarxiv-cs-ai

Beyond Retrieval: Analytic Memory for Multimodal Agents

arXiv:2607.29440v1 Announce Type: new Abstract: Long-term multimodal memory must support not only retrieving relevant information but also computing over observations accumulated across interactions.

researcharxiv-cs-ai

Bridging the Question-Answer Gap in Retrieval-Augmented Generation: Hypothetical Prompt Embeddings

arXiv:2607.29402v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems synergize retrieval mechanisms with generative language models to enhance the accuracy and relevance of r

safetyarxiv-cs-cl

FairFund-Bench: Evaluating Distributive Bias in LLM Resource Allocation

arXiv:2607.28934v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly involved in the distribution of scarce resources, raising concerns about biased allocations based on cha

FibVLA: An Efficient Temporal Vision-Language-Action Model with Fibonacci Sampling

arXiv:2607.29596v1 Announce Type: cross Abstract: Vision-language-action models (VLAs), which leverage the cognition of multimodal information to infer physical-world actions, provide a generalized so

applicationsarxiv-cs-cv

Fragility of Value under Imperfect Alignment

arXiv:2607.28881v1 Announce Type: new Abstract: As more responsibility is placed upon AI systems, it becomes increasingly important to guarantee that these systems are aligned with humanity. A common

Generative AI in Action: Field Experimental Evidence from Alibaba's Customer Service Operations

arXiv:2603.29888v2 Announce Type: replace-cross Abstract: In collaboration with Alibaba, we study how a generative AI assistant affects service performance in e-commerce after-sales operations. In a l

agentsarxiv-cs-ai

HAM-VLN: Harnessing Hierarchical Agentic Memory for Zero-Shot Vision-and-Language Navigation

arXiv:2607.29600v1 Announce Type: new Abstract: Vision-and-language navigation (VLN) enables robots to follow instructions in previously unseen environments. Recently, a training-free paradigm has eme

agentsarxiv-cs-ro

Know It, Act on It: Investigating Memory Utilization in LLM Personalization

arXiv:2607.29433v1 Announce Type: new Abstract: As large language model (LLM) agents evolve into personalized companions, memory has emerged as a core capability. However, LLMs face a knowledge utiliz

agentsarxiv-cs-cl

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction

arXiv:2601.15037v2 Announce Type: replace-cross Abstract: Open-domain Relational Triplet Extraction (ORTE) aims to mine structured knowledge without predefined relation schemas. Large Language Models

researcharxiv-cs-ai

ReSum: Synergizing LLM Reasoning and Summarization with Reinforcement Learning

arXiv:2606.13316v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is a central technique for improving long-horizon reasoning in Large Language Models (LLMs). H

researcharxiv-cs-ai

Hardware

Studying quantization trade-offs for efficient inference deployment in machine translation

arXiv:2607.29397v1 Announce Type: new Abstract: Deploying large language models in realistic server environments poses challenges, as the system needs to provide high-quality responses with low latenc

hardwarearxiv-cs-cl

Hardware

Topology-Aware Data Movement for Disaggregated GPU Inference

arXiv:2607.28633v1 Announce Type: cross Abstract: Disaggregated LLM inference creates a datacenter networking problem that no existing system solves correctly. When prefill and decode run on separate

hardwarearxiv-cs-ai

Local Ai

WaiT for the Signal: Simple Frequency-Aware Flow-Matching

arXiv:2607.28760v1 Announce Type: cross Abstract: As image generation models scale to ever higher resolutions, global coherence, local detail, and texture fidelity become critical axes for generation

local-aiarxiv-cs-ai

Why It Hurts: Identifying the Drivers of Negative Thoughts in Emotional Support Conversations

arXiv:2607.28648v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for emotional support tasks, such as negative thought reframing. This task relies on modifying cogn

WitCert: Sound Runtime Risk Observability and Gating for KV-Cache Quantization

arXiv:2607.28699v1 Announce Type: cross Abstract: KV-cache quantization is validated today by offline benchmark averages; a deployed system cannot tell whether compression is damaging the request it i

A Lightweight Foundation Model for Collider Physics with Multi-Domain Adaptation

arXiv:2607.27501v1 Announce Type: new Abstract: We present a lightweight approach to foundation modeling (extbf{NEXUS}) that leverages pre-trained learning from collider physics data towards out-of-do

researcharxiv-cs-lg

AfriEconQA: A Benchmark for Quantitative and Temporal Reasoning over World Bank Economic Reports

arXiv:2601.15297v3 Announce Type: replace Abstract: Reliable question answering over long institutional documents requires more than topical retrieval: a system must localize the exact passage that su

Ask don't tell: Reducing sycophancy in large language models

arXiv:2602.23971v4 Announce Type: replace-cross Abstract: Sycophancy, the tendency of large language models to favour user-affirming responses over critical engagement, has been identified as an align

AskChem: Claim-Centered Infrastructure for Chemistry Literature Synthesis

arXiv:2607.28618v1 Announce Type: new Abstract: Chemistry literature synthesis often requires assembling specific findings scattered across many publications, yet existing literature-search systems pr

Beyond Frame Selection: Generative Latent Evidence Aggregation for Long-Video Understanding

model-releasesarxiv-cs-cv

arXiv:2607.28516v1 Announce Type: new Abstract: Long-video understanding commonly compresses videos into a small set of frames or visual tokens for answer generation. Existing compact pipelines focus

BioPro: Towards Difference-Aware Gender Fairness for Vision-Language Models

arXiv:2512.00807v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) inherit significant social biases from their training data, notably in gender representation. Current fairness interve

Building a User Foundation Model for the Open Web

arXiv:2607.28019v1 Announce Type: new Abstract: User foundation models have demonstrated strong results in e-commerce and social recommendation, but most industrial deployments assume environments whe

applicationsarxiv-cs-lg

Calibrate Before Reason: Robust Visual Token Reduction against Semantic Drift in VLMs

arXiv:2607.27700v1 Announce Type: new Abstract: Large Vision-Language Models (VLMs) suffer from prohibitive inference overhead due to long sequences of visual tokens. However, existing visual token re

researcharxiv-cs-cv

Local Ai

Capturing Token Tendencies for Training-Free Token Pruning in Multimodal Large Language Models

arXiv:2607.28341v1 Announce Type: new Abstract: While visual token pruning is essential for efficient Multimodal Large Language Models (MLLMs), existing training-free methods suffer from a critical li

local-aiarxiv-cs-cv

ClinLens: Towards Long-Horizon Coding Agents for Longitudinal Multimodal Clinical Data Science

arXiv:2607.26155v1 Announce Type: new Abstract: Clinical data-science agents must transform heterogeneous longitudinal records into auditable analyses, yet existing benchmarks largely isolate medical

Cocktail-Talker: Multi-Speaker Dialog Modeling in Noisy Social Environments with Turn Action GRPO

arXiv:2607.27756v1 Announce Type: cross Abstract: Spoken dialog systems are typically designed for clean, dyadic interactions in which a single user and an assistant take turns speaking. Real-world so

applicationsarxiv-cs-cl

Hardware

Complementary Matrix-Gated QKAN Fast-Weight Programmers for Quantum Dynamics Forecasting

arXiv:2607.27945v1 Announce Type: cross Abstract: Sequence models must decide what to write into memory and what to retain. In quantum and quantum-inspired sequence learning, nonlinear recurrent updat

hardwarearxiv-cs-lg

Compliance2LoRA: On-Demand Safety Alignment on Arbitrary Policy Subsets via Hypernetwork-Generated LoRA Adapters

arXiv:2607.27594v1 Announce Type: new Abstract: Post-training alignment in large reasoning models (LRMs) has significantly improved their adaptability to diverse safety compliance settings. However, a

safetyarxiv-cs-lg

DualAnchor: Preserving Language Priors and Improving Lexical Fidelity in Gloss-Free Sign Language Translation

arXiv:2607.27614v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have led sign language translation (SLT), the task of converting sign-language videos into spoken-langua

safetyarxiv-cs-cl

Efficient LLMs with AMP: Attention Heads and MLP Pruning

model-releasesarxiv-cs-lg

arXiv:2504.21174v2 Announce Type: replace Abstract: Deep learning drives a new wave in computing systems and triggers the automation of increasingly complex problems. In particular, Large Language Mod

Tutorials

Enhancing Scene Transition Awareness in Video Generation via Post-Training

arXiv:2507.18046v2 Announce Type: replace Abstract: Recent advances in AI-generated video have shown strong performance on text-to-video tasks, particularly for short clips depicting a single scene. H

tutorialsarxiv-cs-cv

Fidelity Is Not Safety: Gently-Compressed LLMs Pass Every Data-Free Quality Guard Yet Invent Procedure Steps in Agentic Execution

arXiv:2607.28196v1 Announce Type: new Abstract: Practitioners accept a compressed language model once it clears a stack of data-cheap quality guards: perplexity within a small factor of the original,

safetyarxiv-cs-cl

Generative AI and linguistic diversity in academic writing and publishing: Perspectives from World Englishes

arXiv:2607.28505v1 Announce Type: new Abstract: The rise of generative artificial intelligence (GenAI) in academic writing and publishing (AWP) raises questions about linguistic inclusivity and the le

researcharxiv-cs-cl

GuidedRAG: Semantic Steering of Retrieval-Augmented Generation

arXiv:2607.26071v1 Announce Type: cross Abstract: In this work, we propose GuidedRAG, a novel extension to traditional Retrieval-Augmented Generation (RAG) that introduces a dedicated selection stage

Inference-Time Agentic Decision Rules Beat Longer Evolving Search for Multi-Image Medical Reasoning

arXiv:2607.27564v1 Announce Type: new Abstract: Multi-image medical VQA is not merely a prompt-length problem; it is a fundamental challenge of agentic decision-making. Medical vision-language agents

safetyarxiv-cs-cv

LEDGERMIND: Provenance-Constrained Multimodal Agentic Reasoning with a Structured Evidence Ledger

arXiv:2607.28374v1 Announce Type: new Abstract: Multimodal agents for visual question answering increasingly operate as multi-step trajectories that interleave perception, retrieval, and reasoning, ye

agentsarxiv-cs-lg

LLM2Vec-Gen: Generative Embeddings from Large Language Models

arXiv:2603.10913v3 Announce Type: replace Abstract: Fine-tuning LLM-based text embedders via contrastive learning maps inputs and outputs into a new representational space, discarding the LLM's output

Looped Transformers with Source-Centered State Evolution

arXiv:2607.27656v1 Announce Type: cross Abstract: Looped Transformers create a useful train- and test-time compute axis by reusing the same Transformer block over recurrent depth, increasing effective