All papers

Introduces LatentRiskBench to evaluate safety risks in enterprise agents where action safety depends on latent workflow context.

This paper presents a compact decision model designed to screen malicious agent tool skills with high recall directly from specification files.

This paper provides four auditing tests to determine whether failure-attribution judges actually perform multi-step checks or merely rely on spurious surface cues.

This paper presents an autonomous system combining LiDAR SLAM, visual imagery, and thermal sensing to inspect under-panel components of solar farms.

This paper presents an optimization method that trains models to decide when to invoke reasoning tokens, reducing unnecessary thinking overhead.

This survey analyzes how verifier noise affects reinforcement learning with verifiable rewards across 58 studies and summarizes practical mitigation techniques.

Proposes Diff Diffusion, a diffusion-based model that predicts localized code edit sequences rather than regenerating entire source files.

Introduces FASHION-RQ, an automated framework deriving hierarchical evaluation benchmarks for fashion retrieval directly from catalog structures.

Shows that evil persona vectors extracted from low-resource languages transfer poorly and fail to steer model behavior effectively across languages.

Proposes CoSIR to train multi-agent collaboration policies using information-theoretic surprisal rewards that assess the helpfulness of communication.

Evaluates SHAP attribution methods on sparse autoencoder features, finding weak correspondence with direct causal feature intervention in language models.

The clinical study compares hyperspectral and RGB imaging for intraoperative detection of low-grade gliomas during brain tumor surgery.

HaloBlock allocates adaptive context halo regions across blocks to smooth context transitions and improve diffusion language model decoding.

FETALFusion combines CNN and Mamba architectures to segment fetal heads accurately across different ultrasound acquisition methods.

Virtual Head Attention uses two linear operations to compress key-value cache overhead without increasing training compute or sacrificing output quality.

The paper presents a saliency-guided patch replay method to mitigate catastrophic forgetting in spiking neural networks under tight memory budgets.

The paper proposes predictability-aware supervised fine-tuning to resolve training and inference order mismatches in diffusion language models.

LatentRemedy uses task-conditioned latent tokens to allow safe key-value cache reuse across conflicting tasks in multi-agent systems.

This paper presents an intermediate-representation method to audit whether specific private data was used in LoRA fine-tuning of language models.

This paper shows chain-of-thought monitoring can fail even when reasoning is necessary, proposing a multi-level hierarchy to measure monitorability.

This paper presents an interpretability protocol to monitor latent reasoning dynamics and stability across multiple looped language model families.

This paper shows data ownership determines whether agents should broadcast or relay table queries, and proposes ownership-aware routing.

This paper studies how different framings of user claims induce sycophancy in large language models on factual questions.

This paper introduces a self-distillation method that searches over repeated layer execution orders to create stronger teacher guidance.

TextualRL updates agent context prompts using groupwise trajectory comparisons rather than isolated feedback runs to improve policy adaptation without parameter tuning.

This paper audits a specific scalar feature scaling method to test whether claimed curvature benefits hold for domain generalization benchmarks.

Runoff eliminates option-order bias in multiple-choice model predictions by scoring items independently before running balanced candidate comparisons.

LiFUSE uses frequency-spatial self-supervised pretraining on non-contrast CT scans to improve representation learning for liver disease detection.

FALLNEST condenses large imbalanced wearable motion sensor datasets into small balanced synthetic sets for training fall detection models.

The authors demonstrate that encoding malicious instructions in agent skills bypasses scanners and causes models to execute harmful actions upon decoding.

JevGPT combines fast local evidence assessments with deliberative planning to improve autonomous research agents operating under fixed retrieval budgets.

SimInter simulates conversational recommendation users by conducting synthetic interviews between language models to enrich implicit behavioral profiles.

ShoeFit presents a multi-view dataset and a dual-stream diffusion framework for consistent virtual footwear try-on synthesis.

The authors evaluate how 8-bit and 4-bit quantization methods affect model accuracy across Bavarian, Standard German, and English benchmarks.

The authors propose a probe-based method to measure and calibrate systematic scoring biases in black-box LLM evaluation judges.

The authors trace internal representations across prompt optimization techniques to show how discrete prompts, prompt tuning, and prefixes alter model processing.

The authors reproduce and compare five language model factuality alignment methods on a standardized benchmark across identical base models and evaluation protocols.

The authors introduce a 2.5-billion-parameter bilingual Tamil-English language model trained from scratch with tool-calling capabilities.

The authors analyze readout scaling in sparse autoencoders and propose canonicalized coordinates to improve the recovery of underlying generative factors.

The authors identify a derivation gap where LLM judges reject correct tool arguments lacking direct textual references, and propose a dedicated reference model.

The authors propose evaluating autoformalization faithfulness by aligning phrase-level correspondences between informal mathematics and formal code under contextual interpretations.

The paper analyzes the geometric distribution of language model output logits, identifying a consistent log-linear relationship that emerges during pretraining.

The paper analyzes implicit morals in news coverage of artificial intelligence across four global regions, identifying distinct regional variations in narrative caution.

The paper proposes a framework that uses reasoning graphs to locate claim-level disagreements across sampled traces and correct multi-step language model reasoning.

The paper introduces a benchmark to evaluate how typed decision models handle theory-of-mind reasoning tasks compared to standard generative language models.

The paper evaluates whether self-distillation using privileged information improves reasoning capabilities or reflects overfitting to training tasks.

This paper proposes a system where language model agents write code to create and refine tools directly over raw knowledge-graph files.

The authors reformulate a recurrent video prediction architecture using gated recurrent units to reduce parameters while maintaining spatiotemporal modeling.

This paper investigates whether cloning a familiar voice systematically transfers personality impressions to an embodied virtual character.

The authors propose a training-free method constructing ontology-grounded semantic identifiers to preserve hierarchy in medical concept embeddings.

This paper investigates attention heads responsible for contextual entrainment and evaluates whether their ablation transfers across different semantic relations.

This paper proposes a framework and benchmark for 3D semantic occupancy prediction in autonomous driving using four-dimensional radar tensors.

The authors propose a mixture-of-experts diffusion framework that unifies world prediction and action generation models across shared physical dynamics.

The paper introduces a debugging training algorithm that rewards localized semantic code edits alongside unit test execution.

The paper introduces an auditing framework that identifies failure steps in multi-agent systems by evaluating how information is utilized across handoffs.

The paper presents a runtime system that structures long-horizon agent memory around reusable progress objects rather than raw interaction history.

The paper analyzes how language model aggregation gains divide into candidate additions and context effects under different baseline comparison setups.

The paper presents a framework and benchmark to evaluate whether self-evolving robotic agents genuinely learn reusable skills across tasks.

The paper formalizes a framework and theoretical generalization bounds for evolving an agent's software harness without updating model weights.

The paper examines back-translation using monolingual data as an alternative to continued pretraining for low-resource translation with language models.