Search: “models”

KC-Bench: A Dynamic Interactive Benchmark for Evaluating Knowledge Conflicts in LLM Agents

arXiv:2609.03588v1 Announce Type: new Abstract: As LLMs increasingly act through tools, they must reconcile user instructions, parametric knowledge, and dynamic environmental observations before takin

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KhatianDoc: A Human-Verified Benchmark Diagnosing Multimodal LLM Failure on Bengali Legal Land Records

DGX agent

arXiv:2609.03597v1 Announce Type: new Abstract: Land ownership in Bangladesh is recorded in Ana-Ganda-Kora-Kranti-Til, a base-16 positional fraction system with dedicated Unicode glyphs, no mainstream

model-releasesarxiv-cs-cl

4 Sept 2026

Lngram v2: Latent N-Gram Memory with Interpretable Discrete Representations

arXiv:2609.03426v1 Announce Type: new Abstract: Transformers lack a native lookup mechanism, requiring repeated dense computation to recognize and reuse local static patterns. Lngram v1 introduces tok

Mind the Gap: Robustness Risks in PII Detection Systems

model-releasesarxiv-cs-lg

arXiv:2609.03464v1 Announce Type: new Abstract: Personally Identifiable Information (PII) detection is a foundational component of data protection infrastructure where missed entities constitute direc

Probe Generalization as Subspace Selection for OOD Deception Detection

arXiv:2609.02893v1 Announce Type: new Abstract: Linear probes can be used to detect behaviors and concepts inside language model activations, but may fail to transfer to out-of-distribution examples.

ReCoSplat: Online Feed-Forward Gaussian Splatting via Render-and-Compare

arXiv:2603.09968v2 Announce Type: replace Abstract: Online novel view synthesis requires a model to reconstruct a scene causally from a stream of observations while keeping it renderable at every mome

RecurTrace: Adaptive Latent Reasoning with Loop-Time Memory

arXiv:2609.03379v1 Announce Type: new Abstract: Repeating a small block of middle layers increases a language model's effective inference depth without adding parameters or generating extra tokens, an

researcharxiv-cs-lg

Agents

Speculative Macro Commit for Faster Tool-Using Agents

arXiv:2609.03236v1 Announce Type: new Abstract: Tool-using LLM agents spend wall-clock time not only on model inference but also in serial action--observation turns, where each tool call, environment

agentsarxiv-cs-ai

Applications

The Analyst in the Prompt: Role, Retrieval, and Memory Biases in LLM Financial Analysis

arXiv:2609.03218v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly use user context such as memory, profiles, and role prompts to personalize their responses. This personalizati

applicationsarxiv-cs-cl

Transfiver: Human-AI Co-Inference through a Shared Editable State

arXiv:2609.03797v1 Announce Type: new Abstract: Long-term human-AI interaction is difficult because the information that guides inference is updated implicitly by the model and is not directly inspect

What Else Needs Fixing? Exploring Cost-Effective Test-Time Compute for Revision Propagation in Artifacts Generated Through Conversation

arXiv:2609.03254v1 Announce Type: new Abstract: Large Language Models (LLMs) often help users generate artifacts through iterative cycles of generation and revision in conversation. A challenge here i

Data-Efficient Networks for Multi-Contrast MRI Reconstruction based on a Generalized Content/Style Prior

arXiv:2609.01959v1 Announce Type: cross Abstract: Multi-contrast MR scans contain redundant structural information that can be leveraged during reconstruction and potentially accelerate acquisition ti

Disease Burden over Skin Tone: Decomposing the Dermatology-AI Generalization Gap

arXiv:2609.02111v1 Announce Type: cross Abstract: Dermatology artificial intelligence (AI) models are predominantly trained on light-skinned, cancer-focused image collections, yet they are increasingl

researcharxiv-cs-ai

Diversifying Long Prompt Image Generation through Structured Prompt Embedding Space Sampling

arXiv:2511.20251v2 Announce Type: replace Abstract: Modern text-to-image models produce impressive visual results from richly specified prompts, yet their behavior under long prompts remains insuffici

Entangled Representations Amplify Collateral Damage in Unlearning

arXiv:2609.02285v1 Announce Type: cross Abstract: A long-held intuition in interpretability research is that representational entanglement, the sharing of structure between knowledge domains in a neur

frb100-40 After Two Decades: An Optimality Certificate and a Preregistered Search Study

arXiv:2609.02804v1 Announce Type: cross Abstract: For more than 20 years, the Model-RB benchmark frb100-40 remained an open challenge; since 2014, its public record had stood at 99 of 100 variables. W

Applications

Generating Medical Image Counterfactuals using Causal Explanations

arXiv:2609.02697v1 Announce Type: new Abstract: Deep learning models have achieved impressive performance in medical image diagnosis, yet their deployment in clinical settings remains constrained by l

applicationsarxiv-cs-cv

Geometric Distillation from Rectified Stereo: Leveraging Epipolar Cues for Monocular Depth

arXiv:2607.15600v2 Announce Type: replace Abstract: Monocular depth foundation models have demonstrated remarkable generalization capabilities across diverse environments. However, they continue to st

safetyarxiv-cs-cv

GlyphAnchor: Enhancing Visual Text Rendering via Position-Anchored Glyph Priors

arXiv:2609.02349v1 Announce Type: new Abstract: Rendering accurate text remains difficult for image generation and editing models, especially when the target contains long, complex, and densely arrang

HarmReduction: Benchmarking LLMs in Harm Reduction Information Provision to Support People Who Use Drugs

arXiv:2507.21815v2 Announce Type: replace Abstract: Millions of individuals' well-being are challenged by the harms of substance use. Harm reduction as a public health strategy provides non-judgementa

Harness Engineering in LLM Tool Use via Agent-Native Reusable Tool Primitives

arXiv:2609.01736v1 Announce Type: cross Abstract: Large language models (LLMs) augmented with external tools have demonstrated remarkable capability in solving complex real-world tasks. However, exist

How LLMs Build Fictional Worlds: Setting and Narrative Space in AI-Generated Creative Storytelling

arXiv:2609.02482v1 Announce Type: new Abstract: In this paper, we analyze how Large Language Models (LLMs) employ worldbuilding strategies, focusing on setting as one measurable dimension of storyworl

Lightweight Adaptation of General-Purpose VLMs for Multispectral and SAR Image Understanding

arXiv:2609.02187v1 Announce Type: new Abstract: General-purpose vision-language models (VLMs) now support strong visual recognition, instruction following, and generation. However, most pretrained vis

LLM Watermarking as Big Data Provenance: A Deployment-Oriented Systematization

arXiv:2607.10103v2 Announce Type: replace-cross Abstract: As large language models (LLMs) become widely deployed, their outputs can be copied, transformed, and redistributed at scale without reliable

Measurement-Driven Sub-Network Selection for On-Premise Retrieval-Augmented Factory Agents

arXiv:2609.02760v1 Announce Type: new Abstract: On-premise assistants can give factory workers conversational access to machine documentation, but models capable of the task rarely fit shop-floor hard

SEAL: Reinforcing Global Safety in Mixture-of-Experts through Shared Expert ALignment

arXiv:2609.02293v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) is a scaling architecture for large language models that activates only a small subset of expert modules per token, enabling

SelfLift: Accelerating Few-Step Diffusion via Self-Recovering Resolution Transition

arXiv:2609.02036v1 Announce Type: new Abstract: Few-step diffusion models substantially compress temporal computation, making the spatial cost of each model evaluation an increasingly dominant source

local-aiarxiv-cs-cv

SHARD: Safe and Helpful Alignment via Self-Reframing Distillation

arXiv:2606.15517v2 Announce Type: replace Abstract: Large language models often struggle with sensitive prompts. They may refuse outright, provide generic safety boilerplate, or fail to address the us

safetyarxiv-cs-cl

Similarity-Aware Personalized Federated Learning in Heterogeneous Environments

arXiv:2609.02241v1 Announce Type: new Abstract: Federated Learning (FL) allows decentralized clients to train models collaboratively while preserving data privacy. However, distribution mismatch acros

local-aiarxiv-cs-lg

T2LSC-Bench: Benchmarking Localized Semantic Control in Text-to-Image Generation

arXiv:2609.02255v1 Announce Type: new Abstract: Recent text-to-image models have become increasingly capable of rendering explicit text, but reliable localized text control requires more than generati

The Implications of Linguistic Illegibility for LLM Security

arXiv:2609.02852v1 Announce Type: new Abstract: LLMs are trained to generate natural language. However, various strands of evidence indicate that an LLM's externalized linguistic outputs and mechanist

safetyarxiv-cs-lg

UTP-Bench: Uncertainty-aware Travel Planning Benchmark

arXiv:2609.02421v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently demonstrated strong capabilities in automated travel itinerary generation. However, real- world travel planni

Automated Event Log Generation from Unstructured Text Using Finetuned LLMs

arXiv:2609.01320v1 Announce Type: new Abstract: Process mining (PM) provides a powerful framework for discovering and optimizing operational processes from event data. However, the efficacy of PM tech

researcharxiv-cs-ai

Channel-Adaptive Edge AI: Maximizing Inference Throughput by Adapting Computational Complexity to Channel States

arXiv:2603.03146v2 Announce Type: replace-cross Abstract: Integrated communication and computation (IC^2) has emerged as a new paradigm for enabling efficient edge inference in sixth-generation (6G) n

local-aiarxiv-cs-ai

CoLT-Drive: Counterfactual Long-Tail Benchmarking and Knowledge-Preserving Adaptation for Driving Affordance Prediction

arXiv:2609.00242v1 Announce Type: cross Abstract: Long-tail autonomous driving failures are often framed as rare-object recognition errors. We argue that this view is incomplete: the decision-critical

CopyShield: A Cross-Level Benchmark of Copyright Defenses in LLMs

model-releasesarxiv-cs-lg

arXiv:2609.01161v1 Announce Type: new Abstract: Large language models can reproduce memorized text verbatim, yet copyright defenses are usually evaluated under incompatible protocols. We introduce Cop

DICE: Distilling Classifier-Free Guidance into Text Embeddings

arXiv:2502.03726v3 Announce Type: replace Abstract: Text-to-image diffusion models are capable of generating high-quality images, but suboptimal pre-trained text representations often result in these

safetyarxiv-cs-cv

Disclosure-Gated User Simulation for Companion-Agent Evaluation

arXiv:2609.00982v1 Announce Type: cross Abstract: Using a large language model to play the user is now standard in scalable evaluation. It has a repeatedly diagnosed failure: the simulated user is exc

Does Fault Localization Beat a Fresh Attempt? A Placebo-Controlled Study of Test-Guided Code Repair

arXiv:2609.00854v1 Announce Type: cross Abstract: Fault localization can focus a code model's repair on the statements a failing test implicates, but a targeted edit may succeed merely because it is s

local-aiarxiv-cs-ai

DramaChain Bench: An End-to-End Benchmark for Short-Drama Generation

arXiv:2609.00646v1 Announce Type: new Abstract: Commercial short-drama production follows a multi-stage chain: script, storyboard, keyframe imagery, shot-level video, and the finished short drama. Mos

Fleets Need a Context Plane: Rethinking Cooperative Perception for Autonomous Drones

model-releasesarxiv-cs-ro

arXiv:2609.00659v1 Announce Type: new Abstract: Cooperative perception allows a drone fleet to combine observations from multiple viewpoints. However, existing systems typically fix their feature-shar

From Rollouts to Recipes: Self-Contained Post-Training for LLMs

arXiv:2609.01422v1 Announce Type: new Abstract: Post-training large language models usually applies a single training recipe to all samples, even though the model's own rollouts reveal different sampl

safetyarxiv-cs-cl

Gaussian Core LoRA: Distribution-Aware Dynamic Adaptation for Broad Concept Erasure

arXiv:2609.01433v1 Announce Type: new Abstract: Concept erasure aims to suppress unsafe, privacy-sensitive, or undesirable generations in text-to-image diffusion models while preserving benign semanti

H3-World: Turning Language Understanding into World Control

arXiv:2609.01560v1 Announce Type: cross Abstract: We present H3-World, an efficient framework that turns the 33B MiniMax-H3 video generator into an interactive world model. Our key finding is that, as

researcharxiv-cs-ai

InSight: A Benchmark for Agentic Claim Verification in Interactive Visualizations

arXiv:2609.01383v1 Announce Type: new Abstract: Vision Language Models have demonstrated remarkable proficiency in interpreting static visual artifacts, but modern data analysis is inherently dynamic,

Investigating Linear Probe Robustness to Linguistic Register, Medical Specialty, and Corpus Shifts in Medical QA

arXiv:2609.01361v1 Announce Type: new Abstract: Linear classifiers trained on hidden states of a large language model (LLM), linear probes, can flag factual errors from a single forward pass. Geometri

MemeBridge: A Dataset for Benchmarking and Mitigating the Bidirectional Cultural Gap in Meme Interpretation

arXiv:2609.00491v1 Announce Type: new Abstract: Communicating across cultures is inherently challenging, especially through culturally dense and ambiguous formats like memes. While people expect large

Online Self-Weighted Fine-Tuning

arXiv:2609.00734v1 Announce Type: new Abstract: Standard supervised fine-tuning (SFT) assigns the same explicit loss weight to every expert demonstration, regardless of the model's changing competence

researcharxiv-cs-lg