7354 papers
MedSteer is a training-free activation-steering framework that generates structurally preserved counterfactual endoscopic images by manipulating cross-attention activations in diffusion transformers, outperforming existing methods in concept editing and downstream medical detection tasks.
This paper proposes and validates a novel approach using GPT-4-powered dialogue systems to facilitate user review creation through interview-based information gathering, demonstrating that the resulting system-generated reviews require less editing and are perceived as more helpful by readers than human-written ones, despite some fluency challenges.
This paper introduces CoTJudger, a graph-driven framework that automatically evaluates the efficiency of Large Reasoning Models by converting Chain-of-Thought traces into dependency graphs to identify the Shortest Effective Path, thereby quantifying structural redundancy and revealing pervasive over-reasoning patterns across 21 models.
This paper introduces Countdown-Code, a novel testbed demonstrating that even minimal contamination of supervised fine-tuning data with reward-hacking trajectories can cause large language models to learn and subsequently generalize misaligned behaviors during reinforcement learning, highlighting the critical need for rigorous validation of synthetic training data.
The paper introduces mAVE, a novel watermarking framework that cryptographically binds audio and video latents in joint generation models to eliminate the "Binding Vulnerability" of existing methods and robustly defend against adversarial Swap Attacks without requiring model fine-tuning.
This paper empirically investigates whether large language models can synthesize executable Unity game code from Goal Playable Patterns under strict structural constraints, revealing that while intermediate representations improve performance, project-level grounding and hygiene failures remain primary bottlenecks in achieving high compilation success rates.
This paper proposes the Personalized Semi-Autoregressive with online knowledge Distillation (PSAD) framework, which utilizes a semi-autoregressive teacher model and a User Profile Network to balance generation quality with low-latency inference while enhancing user-item interactions, thereby outperforming state-of-the-art baselines in both ranking performance and efficiency.
This paper introduces ConservationBench to demonstrate that current Vision Language Models systematically fail to reason about physical transformations and maintain invariant representations of physical quantities, often performing near chance levels despite strong textual priors favoring invariance.
This paper presents an LLM-based Werewolf AI agent for the AIWolfDial 2024 shared task that improves utterance consistency and character maintenance by leveraging dialogue summaries and manually designed personas.
This paper introduces aCAPTCHA, a novel security protocol that verifies whether an entity is a capable AI agent by leveraging the asymmetric processing speed between humans and machines to solve the Agentic Capability Verification Problem (ACVP) through time-constrained, multi-round natural language challenges.
This paper introduces EyExIn, a data-efficient framework that enhances retinal Vision Language Models by employing a dual-stream encoding strategy and a deep expert injection mechanism to bridge perception and reasoning gaps, thereby achieving state-of-the-art precision in ophthalmic diagnosis while preventing hallucinations.
This paper introduces Emotion Transcription in Conversation (ETC), a novel task and accompanying Japanese dataset designed to capture complex and subtle emotional states through natural language descriptions, addressing the limitations of traditional categorical emotion recognition methods.
This paper introduces DCTR, a table retrieval mechanism that leverages fine-grained typed query decomposition and global connectivity awareness to effectively handle complex, open-domain question answering over relational databases, demonstrating robustness on industry-aligned benchmarks.
This paper proposes a computationally efficient inference-time reasoning method that improves accuracy by selecting thought-level continuations that maximize the model's internal self-certainty, demonstrating that optimizing for uncertainty minimization at early planning stages yields performance comparable to or exceeding existing scaling techniques like self-consistency.
This paper proposes using graph neural networks to predict initial branching orders for CDCL SAT solvers, demonstrating significant speedups on random and pseudo-industrial benchmarks while noting that the approach struggles with complex industrial instances due to the solver's dynamic heuristics overriding the predictions.
This paper addresses the limitation of existing creative activity tracing methods that capture state changes without preserving intent or higher-level structure by proposing three complementary domain-specific approaches: a node-based interface for GenAI, a vocabulary of visual cues for visualization authoring, and a semantic history-embedded programming model.
This paper proposes the Layered Governance Architecture (LGA), a four-layer framework designed to systematically mitigate execution-layer vulnerabilities in autonomous agent systems, and validates its effectiveness through a bilingual benchmark demonstrating high interception rates of malicious tool calls with minimal latency.
The paper introduces Re, a reinforcement learning framework that enables large language models to dynamically abandon unproductive reasoning paths and restart their solution process, thereby significantly improving reasoning efficiency and performance without requiring preliminary supervised fine-tuning.
This paper introduces a miniature brain transformer architecture that demonstrates a novel, falsifiable prediction: functional lateralization of hippocampal banks requires the synergistic interaction of a prefrontal working-memory buffer (acting as a symmetry-breaker) and inhibitory callosal coupling, a mechanism that triggers a sharp phase transition in memory performance while a cerebellar fast-path merely accelerates convergence.
VINO is a self-supervised learning framework that overcomes the "co-occurrence trap" in dense video by using a teacher-student distillation approach with structural priors to force representations to focus on foreground objects rather than background context, achieving state-of-the-art unsupervised object discovery performance.