arXiv:2508.08127v2 Announce Type: replace Abstract: The security of LLM-based multi-agent systems (MAS) is critically threatened by propagation vulnerability, where malicious agents can distort collective decision-making through inter-agent message interactions. While existing supervised defense methods demonstrate promising performance, they may be impractical in real-world scenarios due to their heavy reliance on labeled malicious agents to train […]
IntentVLM: Open-Vocabulary Intention Recognition through Forward-Inverse Modeling with Video-Language Models
arXiv:2604.24002v1 Announce Type: cross Abstract: Improving the effectiveness of human-robot interaction requires social robots to accurately infer human goals through robust intention understanding. This challenge is particularly critical in multimodal settings, where agents must integrate heterogeneous signals including text, visual cues to form a coherent interpretation of user intent. This paper presents IntentVLM, a novel […]
DNA Ternary Full Adder
arXiv:2603.11684v2 Announce Type: replace Abstract: As transistor dimensions continue to shrink, binary devices are rapidly approaching their fundamental limits in power density. In response, multi-valued systems have attracted significant attention due to their enhanced information density. Among these, the ternary system stands out as the most practical option, being the closest integer base to (e), […]
An Integrated Deep-Learning Framework for Peptide-Protein Interaction Prediction and Target-Conditioned Peptide Generation with ConGA-PepPI and TC-PepGen
arXiv:2604.18467v2 Announce Type: replace-cross Abstract: Motivation: Peptide-protein interactions (PepPIs) are central to cellular regulation and peptide therapeutics, but experimental characterization remains too slow for large-scale screening. Existing methods usually emphasize either interaction prediction or peptide generation, leaving candidate prioritization, residue-level interpretation, and target-conditioned expansion insufficiently integrated. Results: We present an integrated framework for early-stage peptide […]
GWT: Scalable Optimizer State Compression for Large Language Model Training
arXiv:2501.07237v5 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse natural language processing benchmarks. However, the escalating scale of model parameters imposes prohibitive memory overheads during training, especially when employing stateful optimizers such as Adam. Conventional memory-efficient strategies, typically involving singular value decomposition (SVD) or weight freezing, often incur non-negligible […]
EPM-RL: Reinforcement Learning for On-Premise Product Mapping in E-Commerce
arXiv:2604.23993v1 Announce Type: cross Abstract: Product mapping, the task of deciding whether two e-commerce listings refer to the same product, is a core problem for price monitoring and channel visibility. In real marketplaces, however, sellers frequently inject promotional keywords, platform-specific tags, and bundle descriptions into titles, causing the same product to appear under many different […]
Reliable Microservice Tail Latency Prediction via Decoupled Dual-Stream Learning and Gradient Modulation
arXiv:2508.01635v2 Announce Type: replace-cross Abstract: Microservice architectures enable scalable cloud-native applications; however, the distributed nature of these systems complicates the maintenance of strict Service Level Objectives. Accurately predicting window-level P95 tail latency remains difficult due to the complex interactions between software workload propagation and infrastructure resource limits. Existing predictive models struggle to capture these dynamics […]
Towards Disentangled Preference Optimization Dynamics Beyond Likelihood Displacement
arXiv:2604.18239v2 Announce Type: replace-cross Abstract: Preference optimization is widely used to align large language models (LLMs) with human preferences. However, many margin-based objectives suppress the chosen response along with the rejected one, a phenomenon known as likelihood displacement, and no general mechanism currently prevents this across objectives. We bridge this gap by presenting a unified […]
Think-at-Hard: Selective Latent Iterations to Improve Reasoning Language Models
arXiv:2511.08577v2 Announce Type: replace-cross Abstract: Improving reasoning abilities of Large Language Models (LLMs), especially under parameter constraints, is crucial for real-world applications. Looped transformers address this by performing multiple latent iterations to refine each token beyond a single forward pass. However, we identify a latent overthinking phenomenon: most token predictions are already correct after the […]
Fix Initial Codes and Iteratively Refine Textual Directions Toward Safe Multi-Turn Code Correction
arXiv:2604.23989v1 Announce Type: cross Abstract: Recent work on large language models (LLMs) has emphasized the importance of scaling inference compute. From this perspective, the state-of-the-art method Scattered Forest Search (SFS) has been proposed, employing Monte Carlo Tree Search with carefully crafted initial seeds and textual optimization for multi-turn code correction. However, its complexity makes it […]
MirrorMark: A Distortion-Free Multi-Bit Watermark for Large Language Models
arXiv:2601.22246v2 Announce Type: replace-cross Abstract: As large language models (LLMs) become integral to applications such as question answering and content creation, reliable content attribution has become increasingly important. Watermarking is a promising approach, but existing methods either provide only binary signals or distort the sampling distribution, degrading text quality; distortion-free approaches, in turn, often suffer […]
Training-inference input alignment outweighs framework choice in longitudinal retinal image prediction
arXiv:2604.16955v2 Announce Type: replace-cross Abstract: Predicting disease progression from longitudinal imaging is useful for clinical decision making and trial design. Recent methods have moved toward increasing generative complexity, but the conditions under which this complexity is necessary remain unclear. We propose that generative complexity should match the entropy of the predictable component of a task’s […]