Search: “llm”

Dear Algo: A Precision-First Agentic Intent Layer for Unified Search and Recommendation

arXiv:2608.15877v1 Announce Type: new Abstract: Search and recommendation serve a shared discovery objective but encode intent differently. We study this boundary through Dear Algo on Threads, a deplo

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Does generative AI supersede supervised XMLC? A Benchmark Study on Automated Subject Indexing with German Scientific Literature

DGX agent

arXiv:2607.14882v2 Announce Type: replace-cross Abstract: With a large controlled vocabulary as the label set, the task of automated subject indexing in a library can be understood as a multi-label cl

model-releasesarxiv-cs-ai

18 Aug 2026

Does the Proof Prove It That Way? Faithful Formalization of Elements Proofs

arXiv:2608.15432v1 Announce Type: new Abstract: In formal verification, both the autoformalization of statements and automated proof search have been studied extensively. While automated proof search

Hardware

Efficient Block-Layer Parallel Inference for Vision-Language-Action on Hybrid Architectures

arXiv:2608.14586v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are becoming a promising paradigm for autonomous driving, but their deployment on existing vehicle platforms remai

hardwarearxiv-cs-ai

L3Cube-IndicQuest v2: A Large-Scale Multilingual Benchmark for Evaluating Factual Knowledge of Large Language Models Across Indic Languages

model-releasesarxiv-cs-cl

arXiv:2608.15535v1 Announce Type: new Abstract: We present L3Cube-IndicQuest v2, a large-scale gold-standard multilingual question-answering benchmark for evaluating the India-specific factual knowled

Large Language Model Assisted Operational Monitoring for Battery Energy Storage System Integrated Power Distribution Networks

arXiv:2608.15396v1 Announce Type: new Abstract: Battery energy storage systems (BESS) are increasingly used in distribution networks for voltage regulation and demand response, which increases the vol

Leveraging Machine Unlearning for Cost-Efficient Preference Alignment

arXiv:2504.06659v2 Announce Type: replace-cross Abstract: Despite advances in Preference Alignment (PA) for Large Language Models (LLMs), mainstream methods like reinforcement learning with human feed

SKILL: Self-correcting Knowledge-guided Iterative Large Language Model Agent for Logic Optimization

arXiv:2608.14579v1 Announce Type: new Abstract: Logic synthesis optimization poses significant challenges due to exponentially growing search spaces, sparse reward signals, and diverse logic structure

STAIR: Semantic-Temporal Automaton for Interpretable Reasoning in Temporal Question Answering

arXiv:2608.16224v1 Announce Type: cross Abstract: By leveraging large-scale pretraining, LLMs can interpret diverse temporal expressions and question formulations without task-specific training. Howev

SysEvolve: An AI-native, safe, autonomous adversarial attack-defense co-evolutionary system

arXiv:2608.15012v1 Announce Type: cross Abstract: The rapid advancement of large language models (LLMs) has created a growing asymmetry in cybersecurity, where attack accelerates toward autonomous exe

When Do Explanations Help In-Context Learning? A Comparative Study of Natural Language Explanation Types and Faithfulness

arXiv:2608.16627v1 Announce Type: cross Abstract: Natural language explanations (NLEs) are increasingly used as inputs, for example, as few-shot rationales that influence model behavior in in-context

AdsWorldEngine: A Self-Evolving Conversational Advertising Agent through Orchestrator and Tool Coevolution

arXiv:2608.13833v1 Announce Type: cross Abstract: Conversational advertising aims to deliver useful ads within multi-turn assistant interactions. Unlike conventional query-based advertising, where the

Boosting Data Augmentation with Stochastic Weight Averaging

arXiv:2608.14373v1 Announce Type: new Abstract: The symmetries of a learning task have become an important factor in designing modern deep learning solutions. Data augmentation is a straightforward an

researcharxiv-cs-lg

Applications

Building AI-Intensive Software with AI: Early Results and a Cautionary Tale on Measuring Development Cost

arXiv:2608.13730v1 Announce Type: cross Abstract: Empirical reports on the true cost of AI-intensive software development remain scarce, and the few that exist are easy to get wrong in ways that never

applicationsarxiv-cs-ai

Capacity-Dependent Effects of Data Selection for Reasoning

arXiv:2608.13721v1 Announce Type: cross Abstract: In reasoning supervised fine-tuning, candidate responses for the same instruction can differ substantially in how well they match the student's curren

Consensus-gated Multi-Agent Neural Architecture Search for Seismic Fault Segmentation

model-releasesarxiv-cs-cv

arXiv:2608.13889v1 Announce Type: new Abstract: Neural networks for seismic fault segmentation are often borrowed from computer vision and medical imaging domains where they train under relatively muc

Context Aware AI Assistant and AR Interface for Lunar Extravehicular Activity (EVA) Procedural Guidance

arXiv:2608.13589v1 Announce Type: cross Abstract: As human space exploration returns to the Moon, astronauts need rapid access to procedural information during extravehicular activities (EVAs), where

Don't Claim Benchmark-Oriented Optimization Improves General Coding Capability -- Diverse Evaluation Is Required

arXiv:2608.13566v1 Announce Type: cross Abstract: Post-training papers, model cards, and blog posts often treat scores on a small set of coding benchmarks (e.g., SWE-bench and LiveCodeBench) as eviden

Fine-Tuning Qwen3-27B for C-to-Rust Code Translation: A Three-Stage Curriculum of Pretraining, Debugging-Aware SFT, and Task-Specific SFT

arXiv:2608.13681v1 Announce Type: cross Abstract: Translating C code into safe, idiomatic Rust is a longstanding software-engineering goal because it can eliminate entire classes of memory-safety vuln

FreeBalance: Pre-Routing Online Moe Load Balancing via Residual Workload Prediction

arXiv:2608.14205v1 Announce Type: new Abstract: Load imbalance poses a major bottleneck to the efficiency of expert parallelism in distributed inference of Mixture-of-Experts (MoE) models. The most he

GALA: Generation-Aware Cross-Modal Alignment for Text-to-Time-Series Synthesis

arXiv:2608.13741v1 Announce Type: new Abstract: Synthesizing time series from natural language is emerging as the most expressive form of controllable time series generation. However, existing text-co

safetyarxiv-cs-cl

Local Ai

HAM-RAG: Hierarchy-Aware Multimodal RAG for Structure-Faithful Interleaved Generation

arXiv:2608.14032v1 Announce Type: cross Abstract: Existing multimodal RAG methods often flatten structured documents into isolated text and image units, weakening the source organization and local tex

local-aiarxiv-cs-ai

HELIX: Model-Harness Co-evolution for Recursive Self-Improvement

arXiv:2608.13951v1 Announce Type: new Abstract: Scaling agent capability has largely focused on improving the model, yet an interactive agent acts through a runtime harness that mediates context, tool

Local Ai

hint^2: Hierarchical World Models for Inference-Time Temporal Logic Guidance

arXiv:2608.13678v1 Announce Type: cross Abstract: A central goal of robot learning is to enable robots to execute rich instructions specified at runtime. Large-scale language-conditioned policies have

local-aiarxiv-cs-lg

Information Satisfaction: A Reader-Centered Axis for Summarization Evaluation

arXiv:2608.14457v1 Announce Type: new Abstract: The majority of work on summarization evaluation focuses on general summary quality (e.g., ROUGE, BERTScore) or specific desired properties (e.g., reada

researcharxiv-cs-cl

Local and Global Regimes of Geometric Complexity in Language Model Representations

model-releasesarxiv-cs-cl

arXiv:2608.14361v1 Announce Type: new Abstract: Intrinsic dimensionality (ID) is widely used to probe the representational complexity of language models, but it remains unclear whether ID differences

LP-NAS: Linear Programming-based Neural Architecture Search

arXiv:2608.14472v1 Announce Type: cross Abstract: Neural Architecture Search (NAS) aims to automate neural network architecture design, reducing reliance on human expertise. Among the various NAS meth

MACS: A Hybrid Multi-Agent Framework for Reliable Conversational E-Commerce Recommendation

arXiv:2608.14068v1 Announce Type: cross Abstract: Conversational recommendation for e-commerce is increasingly mediated by large language models (LLMs), yet many real-world deployments operate under a

Measuring Fairness in Large Audio Language Models via Semantic-Aware Bias Estimation

arXiv:2608.13624v1 Announce Type: cross Abstract: Large Audio Language Models (LALMs) have seen increasing use for audio understanding tasks such as speech recognition and audio question answering, ra

MemoryLake on MemoryArena: A Matched Study of Agent Memory Backends

arXiv:2608.13883v1 Announce Type: new Abstract: Most agent-memory benchmarks test post-hoc recall, whereas MemoryArena evaluates whether memory supports interdependent, multi-session task completion.

Mind the Long Tail: Understanding the Difficulty of Delay Detection in Business Processes

arXiv:2608.14367v1 Announce Type: cross Abstract: The early detection of delayed cases in business processes is a critical capability for organizations. Predictive process monitoring (PPM) supports th

On the Brittleness of Maximum Likelihood Estimation for Gaussian Process Hyperparameter Optimization

arXiv:2608.13793v1 Announce Type: cross Abstract: Machine learning (ML) has become an indispensable part of modern engineering design workflows. A crucial step in training an ML model is the selection

researcharxiv-cs-lg

Polaris : Multi Agentic System for Conversational Enterprise Analytics

arXiv:2608.14246v1 Announce Type: new Abstract: In today's fast-paced environment, the ability to swiftly access, understand, and act on data is no longer optional; it is essential. Yet most organizat

QUASAR: Lowering the Loss Floor of Quantization-Aware Training with Loss-Aware Reconstruction

model-releasesarxiv-cs-cl

arXiv:2608.13966v1 Announce Type: cross Abstract: As large language model inference shifts toward lower precision, post-training quantization (PTQ) becomes increasingly brittle, making quantization-aw

QuaSAR: Quantization Compensation via Stable Activation-Aware Rank Truncation

arXiv:2608.14149v1 Announce Type: new Abstract: Recent training-free post-training quantization methods restore model accuracy through closed-form residual compensation. To constrain additional model

Regime-Conditional Verification: Correctness Estimation for Adapting and Monitoring Safety Classifiers

arXiv:2608.14089v1 Announce Type: new Abstract: Safety classifiers deployed with large language models often fail for two reasons: their decisions reflect the policy learned during training rather tha

Local Ai

Residual Dominance as a Structural Account of Last-Item Reliance in Causal Self-Attention Recommenders

arXiv:2608.14021v1 Announce Type: new Abstract: Transformer-based sequential recommenders with causal self-attention often rely heavily on the most recent interaction at inference time, but how this b

local-aiarxiv-cs-ai

Rewrite Once, Validate Anywhere: Producing OWL-Aware SHACL Constraints (Extended Version)

arXiv:2608.14104v1 Announce Type: cross Abstract: The Shapes Constraint Language (SHACL) is a W3C recommendation to express syntactic constraints, called shapes, on RDF graphs. SHACL validators are us

Rollplex: Cross-Phase GPU Spatial Sharing for Vision Language Model Post-Training

model-releasesarxiv-cs-lg

arXiv:2608.14498v1 Announce Type: new Abstract: Vision-language models (VLMs) enable embodied agents to reason and act from visual observations and language instructions. Reinforcement learning (RL) p

The Metacognitive Bottleneck: Japanese Riddles Reveal Fundamental Limits of Machine Insight and Self-Evaluation in Reasoning AI

arXiv:2509.14704v3 Announce Type: replace Abstract: Benchmark saturation and training-data contamination increasingly obscure whether reported gains in large language models (LLMs) reflect genuine adv

TimeSage-EV: A Live Benchmark for Agentic Time Series Analysis in Evolving Environments

arXiv:2608.14270v1 Announce Type: new Abstract: Time series analysis in high-stakes domains relies on recurring data releases, where new observations can alter the evidence base and the validity of la

Towards Efficient Multimodal and Multilingual Opinion Extraction for STI: A QLoRA-Based Fine-Tuning Approach

arXiv:2608.14152v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have reshaped semantic analysis. Opinion Extraction (OE) for Science and Technology Intelligence (STI) r

Tripwire: Triggering Aligned Refusal via Statistically Certified Safety Neurons

arXiv:2608.14392v1 Announce Type: new Abstract: Neuron- and path-level interventions offer the finest-grained route to defending large language models (LLMs) against jailbreak attacks, yet existing me

Wrong but Useful: Trajectory Value Beyond Answer Correctness in Multi-Agent Messages

arXiv:2608.14375v1 Announce Type: new Abstract: Multi-agent reasoning systems often use agreement, confidence, or automated scores to decide which messages should shape a final answer. Such filtering

A Cloud-Edge System for Multimodal Clinical Screening in Resource-Constrained Rural Settings

arXiv:2608.12745v1 Announce Type: new Abstract: Medical AI has demonstrated specialist-level diagnostic accuracy, yet these capabilities remain largely inaccessible in resource-constrained rural setti

researcharxiv-cs-lg

A Probe Direction Is a Property of Its Prompt

model-releasesarxiv-cs-lg

arXiv:2608.13329v1 Announce Type: new Abstract: A model that behaves differently when it senses it is being tested would undermine the evaluations we rely on, so recent work has sought to read that se

AI and Consumer Rights in India Working Paper

arXiv:2608.12863v1 Announce Type: new Abstract: As AI systems proliferate in consumer facing applications, questions about liability for AI related harms remain unresolved. This working paper examines

Algebraic Decomposition Theory for Transformer Length Generalization

arXiv:2608.13433v1 Announce Type: cross Abstract: Transformer-based language models are known to sometimes generalize to sequences longer than seen during training, but we lack a precise characterizat