Search: “context”

Beyond Factual QA: Mentorship-Oriented Question Answering over Long-Form Multilingual Content

arXiv:2601.17173v2 Announce Type: replace-cross Abstract: Question answering systems are typically evaluated on factual correctness, yet many real-world applications-such as education and career guida

Human

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Local Ai

Beyond Parallel Blindness: Information Floors and Model Gaps in Block Drafting

DGX agent

arXiv:2608.27339v1 Announce Type: cross Abstract: Block drafters propose several tokens in one forward pass, before earlier target tokens are realised. Their rejection mixes two losses: missing within

local-aiarxiv-cs-cl

28 Aug 2026

Beyond Reflection: Affirmation as a Promising Behavioral Marker Associated with Quality in Text-Based Counseling

arXiv:2608.26689v1 Announce Type: new Abstract: While AI-assisted text-based counseling is gaining attention, it remains empirically unclear which counselor behaviors are associated with higher dialog

Industry

Blue Yonder redesigns supply chain operations around AI agents

Permanent disruption has turned supply chain management into a boardroom concern, sitting alongside security as tariff volatility, reshoring pressure and geopolitical shocks reset the cost of getting

industrysiliconangle

Applications

BPMN4CAI: A BPMN Extension for Modeling Dynamic Conversational AI

arXiv:2608.27149v1 Announce Type: new Abstract: Conversational AI systems, such as chatbots and virtual assistants, are becoming increasingly important to digital business processes. However, the esta

applicationsarxiv-cs-ai

BTS-AgentBench: A Deterministic, Replayable Pipeline from Read-Only Telemetry Logs to Agent Benchmarks

arXiv:2608.27334v1 Announce Type: new Abstract: Industrial sites contain large volumes of read-only telemetry, but few benchmarks specify how to compile these records into executable multi-turn agent

CritICL: Inference-Time Weak-to-Strong Generalization from Small Language Model Failure Modes

arXiv:2608.27455v1 Announce Type: new Abstract: Recent advances in inference-time scaling have significantly improved the reasoning performance of large language models (LLMs). However, these methods

DEEPCHART: How Far are LLMs from Faithful Data-Science Chart Generation?

arXiv:2608.26757v1 Announce Type: new Abstract: Faithful chart generation in real-world data-science workflows requires grounding visualizations in scattered evidence, computing chart-ready quantities

Discovering Relationships in Data Lakes Using Large Language Models: An Industrial Case

arXiv:2608.26750v1 Announce Type: new Abstract: Data lakes rely on metadata to remain usable, yet this meta data is often limited or weakly informative for column relationship discovery, especially in

Local Ai

DSA: Evidence-Aware LLM-Agent Orchestration for Multi-Market Stock Research

arXiv:2608.26990v1 Announce Type: new Abstract: Large language models can summarize financial information, but an operational stock-research system must first assemble heterogeneous evidence, expose u

local-aiarxiv-cs-ai

GameWAM: A World Action Model for Video Games

arXiv:2608.26200v1 Announce Type: new Abstract: Modern video games combine first-person perception, rapid visual changes, persistent world state, and heterogeneous native controls. Existing game agent

hoBIT: A Profile-Aware Retrieval-Augmented Chatbot for University Academic Advising

arXiv:2608.26604v1 Announce Type: cross Abstract: In university academic advising, identical questions can require different answers depending on a student's department, admission cohort, and degree p

I reverse-engineered an NPU vendor's engine format (int8 weights stored as two nibble planes) to run GGUFs with no model conversion — now 1.5× faster than the vendor's own runtime

I've been running Qwen3-0.6B on the M5Stack LLM-8850 card (Axera AX8850 NPU, 24 TOPS, 8GB LPDDR4x) hosted by a Raspberry Pi 5 — as a llama.cpp backend. The problem: the vendor stack requires convertin

Importance Scoring of Transformer Attention Heads in Learning Tabular Data

arXiv:2608.27241v1 Announce Type: new Abstract: Computationally demanding and opaque deep learning models can be better understood and optimized by analyzing how they transform data. While deep transf

Instruction Quality Matters: Refining Instructions for Effective Preference Learning

arXiv:2608.26779v1 Announce Type: new Abstract: Preference learning optimizes models using response pairs, yet the informativeness of these pairs is fundamentally shaped by the instructions from which

safetyarxiv-cs-cl

Learning the ARTS of Search for Automated Discovery

arXiv:2606.21891v2 Announce Type: replace Abstract: Scientific discovery can be formulated as an iterative search process over the space of hypotheses and experiments. Contemporary methods navigate th

MACGen: Toward Functionally Correct and Secure Code Generation via Multi-Agent Collaboration

arXiv:2608.25457v2 Announce Type: replace-cross Abstract: Despite their strong ability to generate code, large language models often fail to produce secure code, as their outputs frequently contain se

agentsarxiv-cs-ai

Making Latent Evolution Explicit: Operator-Structured Transitions for World Action Models

arXiv:2608.27259v1 Announce Type: new Abstract: World Action Models (WAMs) augment robot policies by predicting how task-relevant scene states may evolve under interaction. Recent WAMs increasingly pe

safetyarxiv-cs-lg

Mitigating Fabrication in Multi-Stage LLM Pipelines for Hiring: An Empirical Evaluation of Prompt Guardrails and Human-in-the-Loop Checkpoints

arXiv:2608.26171v1 Announce Type: cross Abstract: Multi-stage LLM hiring pipelines (resume improvement, interview question generation, answer feedback) can fabricate credentials, inflate qualifiers, a

safetyarxiv-cs-cl

Applications

Multi-Image Visual Token Pruning in Large Visual Language Models

arXiv:2608.26806v1 Announce Type: new Abstract: With the growing demand for processing multiple image sequences in real-world applications, various visual token pruning methods have emerged to mitigat

applicationsarxiv-cs-cv

Ornith-1.5-35B-A3B on 8 GB VRAM: I think I've found my sweet spot

A few days ago I posted asking what people considered the best local model for an 8 GB VRAM GPU. At the time, my personal sweet spot was Qwen3.6-35B-A3B, for agentic coding with Pi.dev. Well… Thanks t

PACE: A Unified Condense-and-Extract Paradigm for Fast VLM Inference

arXiv:2608.27206v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) demonstrate exceptional visual reasoning capabilities, yet their inference costs escalate rapidly with the proliferation

Planting a Latent Variable in Natural-Looking Text: a More Realistic Test of Belief States in LLMs and Their Link to Concept Geometry

arXiv:2608.26887v1 Announce Type: new Abstract: LLMs are thought to track 'belief states,' i.e., running probability distributions over the latent variables that govern language (Shai et al., 2024; Sa

Recovering Expert Critic-Sourced Network Adjacency between Musical Artists from Acoustic Distributions: A Construct-Validity Approach

arXiv:2608.27291v1 Announce Type: cross Abstract: Music recommendation relies primarily on two signals: user-item interactions, which fail in the cold-start regime, and intrinsic musical content, avai

[Release] SOTA GGUFs for Qwen3.8-27B: GSQ-RCO at 2.5 to 3.0 bpw

We're releasing Qwen3.8-27B quantized with our newest methods, GSQ + RCO. Higher-quality models, same file size, now with the search and the quantizer both learned. What's inside: GSQ (Gumbel-Softmax

Rethinking Message Passing as Retrieval for Text-Attributed Graph Learning

arXiv:2608.26732v1 Announce Type: cross Abstract: Graph neural networks (GNNs) are typically conceptualized as message-passing neural networks, yet it remains unclear why neighborhood aggregation reli

Robust Neural Stimulation Response Modeling Through Meta-Learning and Pretraining

arXiv:2608.26649v1 Announce Type: new Abstract: Objective: Model-based closed-loop neural stimulation holds promise for therapeutic applications ranging from Parkinson's disease to sensory restoration

Same Model, Different Harness: Different Coding-Agent Results

arXiv:2608.26218v1 Announce Type: new Abstract: A coding agent combines a model with a harness, which decides what the model sees, which tools it can use, and how the work continues. We ask whether ch

agentsarxiv-cs-ai

Selection Bias Correction in Retail Intelligence

arXiv:2608.26156v1 Announce Type: new Abstract: Retail intelligence often relies on monitoring popular, high-velocity products, potentially biasing economic indicators by ignoring the 'long tail' of n

safetyarxiv-cs-ai

Selective State-Space Adaptation and Retrieval for Language Model Reasoning

arXiv:2607.19326v2 Announce Type: replace Abstract: Low-rank adaptation introduces a static learned update applied identically to every input. The update provides task-level adaptation but does not ex

Self-Augmented Diffusion Guidance for Physics-Informed Generation

arXiv:2608.26748v1 Announce Type: new Abstract: Diffusion models can be used to generate spatiotemporal signals of physical phenomena, such as time-series images of fluid dynamics. However, a major li

Static embeddings offer unmatched throughput, but they also suffer from accuracy loss compared to traditional embedding models. Can we make …

Static embeddings offer unmatched throughput, but they also suffer from accuracy loss compared to traditional embedding models. Can we make them better for retrieval? We tried several things: ✅️ raw m

agentsllamaindex--x

Sycophancy Suppression Can Impair Rational Updating: Anti-Sycophancy Should Preserve the Ability to Update

arXiv:2608.26511v1 Announce Type: new Abstract: Large language models often exhibit sycophancy, revising their answers to align with users when users push back. Such answer flips, however, can arise f

Tabular Deep Learning for Algorithmic Trading: Cross-Regime Bayesian Optimisation for Equity Signal Generation

arXiv:2608.27076v1 Announce Type: new Abstract: Algorithmic trading now represents a market exceeding $20 billion, where even marginal gains in signal robustness can translate into economically signif

Text-to-seed generation: Training-free open-vocabulary seeded semantic segmentation via re-purposing diffusion as text-guided seed generator

arXiv:2608.26624v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation (OVSS) aims to segment image regions corresponding to arbitrary text queries. Although the Segment Anything Model

researcharxiv-cs-cv

Today I hit 181 toks/s (aggregate) on Qwen3.8-Flash-Next on 2x DGX Sparks

Hey all, and hello fellow DGX Spark-ers! Today I managed some pretty crazy numbers: 181 tok/s aggregate on 2× DGX Spark on Qwen3.8-Flash-Next at 512Kcontext (2.8M kvc) I hit 181 tok/s aggregate today

Towards Expert Financial QA via Self-Improving RAG

arXiv:2608.26706v1 Announce Type: new Abstract: Expert-level financial question answering requires both grounded verification to catch numeric hallucinations and audit trails for regulatory compliance

agentsarxiv-cs-cl

Applications

When Is the Sharp Covariance Envelope Tight? Feature-Only Geometry for Volume-Sampled Least Squares

arXiv:2608.26877v1 Announce Type: new Abstract: Prior analyses by Derezinski and Warmuth established all-size sampling identities, selected-OLS unbiasedness, and inverse moments for ordinary volume sa

applicationsarxiv-cs-lg

When Text Misleads: Inconsistent-Aware Reasoning for Audio-Grounded Dialogue

arXiv:2608.27176v1 Announce Type: cross Abstract: Understanding spoken dialogue requires joint reasoning over lexical content and paralinguistic acoustic signals such as emotion and conversational int

A Primer on Computational Semantics for Artificial Intelligence Systems

arXiv:2608.25022v1 Announce Type: new Abstract: As people adopt transformer-based language models (e.g., ChatGPT and Gemini) for an increasing number of use-cases, it is important to know how such mod

Activation Steering Transfer to Agents: One Gain Ratio Does Not Identify Potency and Efficacy

arXiv:2607.09156v2 Announce Type: replace Abstract: Additive activation steering is calibrated in single-turn chat and then deployed inside agent scaffolds. The quantity usually reported for that move

safetyarxiv-cs-lg

Adaptive Bayes exactly tracks information over intrinsic time

arXiv:2607.08789v2 Announce Type: replace Abstract: Bayesian and multiplicative-weights updates reweight experts, models, or actions from sequential feedback. We show that the regret of any such updat

Anchoring Bias in LLM-as-a-Judge Systems: Prior Scores Compromise Evaluation Independence

arXiv:2608.25869v1 Announce Type: new Abstract: Large language models (LLMs) increasingly assess generated content, giving rise to the LLM-as-a-Judge paradigm. These systems now score outputs, filter

safetyarxiv-cs-cl

// Automata from agent traces // How much of your agent's behavior comes from the model, and how much from the harness you wrapped around it…

// Automata from agent traces // How much of your agent's behavior comes from the model, and how much from the harness you wrapped around it? New work collapses an entire corpus of agent traces into a

agentsdair-ai--x

CaSKG: Counterfactual-Causal Skill Graphs for Scalable Agent Skill Retrieval

arXiv:2608.25500v1 Announce Type: cross Abstract: Reusable skill libraries allow large language model (LLM) agents to reuse procedural knowledge across tasks, but they also turn memory access into a c

Hardware

DataKernelBench: Can LLMs Optimize Database Queries on GPUs?

arXiv:2608.25061v1 Announce Type: new Abstract: GPUs increasingly accelerate database systems, but query-specific peak performance still often relies on hand-written kernels. Existing LLM kernel bench

hardwarearxiv-cs-cl

Do Vision-Language Models Agree on the Affective Qualities of Shape? A Cross-Model Audit for Generative Design Interfaces

arXiv:2608.25876v1 Announce Type: cross Abstract: Generative design interfaces increasingly expose semantic controls that let users steer output with concepts such as 'more elegant' or 'more minimalis

researcharxiv-cs-cv

GreenLeaf Law Embed Tiny: A Compact Embedding Model for Legal Domain Retrieval

arXiv:2608.24936v1 Announce Type: cross Abstract: We present GreenLeaf Law Embed Tiny, a 0.6B parameter embedding model for legal domain retrieval. GreenLeaf-Tiny achieves 75.11% on the Massive Legal