arXiv:2603.06854v1 Announce Type: cross Abstract: Multimodal large language models can exhibit text dominance, over-relying on linguistic priors instead of grounding predictions in non-text inputs. One example is large audio-language models (LALMs) where decisive audio evidence can be under-utilized even when it contains important information. To address this issue we use mechanistic interpretability to identify a […]
Batch-of-Thought: Cross-Instance Learning for Enhanced LLM Reasoning
arXiv:2601.02950v2 Announce Type: replace Abstract: Current Large Language Model reasoning systems process queries independently, discarding valuable cross-instance signals such as shared reasoning patterns and consistency constraints. We introduce Batch-of-Thought (BoT), a training-free method that processes related queries jointly to enable cross-instance learning. By performing comparative analysis across batches, BoT identifies high-quality reasoning templates, detects errors […]
Elenchus: Generating Knowledge Bases from Prover-Skeptic Dialogues
arXiv:2603.06974v1 Announce Type: cross Abstract: We present Elenchus, a dialogue system for knowledge base construction grounded in inferentialist semantics, where knowledge engineering is re-conceived as explicitation rather than extraction from expert testimony or textual content. A human expert develops a bilateral position (commitments and denials) about a topic through prover-skeptic dialogue with a large language […]
Identifying genes associated with phenotypes using machine and deep learning
arXiv:2603.06804v1 Announce Type: new Abstract: Identifying disease-associated genes enables the development of precision medicine and the understanding of biological processes. Genome-wide association studies (GWAS), gene expression data, biological pathway analysis, and protein network analysis are among the techniques used to identify causal genes. We propose a machine-learning (ML) and deep-learning (DL) pipeline to identify genes […]
Can Safety Emerge from Weak Supervision? A Systematic Analysis of Small Language Models
arXiv:2603.07017v1 Announce Type: cross Abstract: Safety alignment is critical for deploying large language models (LLMs) in real-world applications, yet most existing approaches rely on large human-annotated datasets and static red-teaming benchmarks that are costly, difficult to scale, and slow to adapt to evolving model behaviors. Moreover, overly conservative safety mechanisms can reduce model usefulness by […]
Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation
arXiv:2603.07107v1 Announce Type: cross Abstract: Generative models offer a promising paradigm for the final stage reranking in multi-stage recommender systems, with the ability to capture inter-item dependencies within reranked lists. However, their practical deployment still faces two key challenges: (1) an inherent conflict between achieving high generation quality and ensuring low-latency inference, making it difficult […]
Making AI Evaluation Deployment Relevant Through Context Specification
arXiv:2603.06811v1 Announce Type: new Abstract: With many organizations struggling to gain value from AI deployments, pressure to evaluate AI in an informed manner has intensified. Status quo AI evaluation approaches mask the operational realities that ultimately determine deployment success, making it difficult for decision makers outside the stack to know whether and how AI tools […]
VINO: Video-driven Invariance for Non-contextual Objects via Structural Prior Guided De-contextualization
arXiv:2603.07222v1 Announce Type: cross Abstract: Self-supervised learning (SSL) has made rapid progress, yet learned features often over-rely on contextual shortcuts-background textures and co-occurrence statistics. While video provides rich temporal variation, dense in-the-wild streams with strong ego-motion create a co-occurrence trap: foreground objects and background context move coherently, encouraging representations to collapse into scene encoders. To […]
A Single Model Ensemble Framework for Neural Machine Translation using Pivot Translation
arXiv:2502.01182v2 Announce Type: replace-cross Abstract: Despite the recent remarkable advances in neural machine translation, translation quality for low-resource language pairs remains subpar. Ensembling multiple systems is a widely adopted technique to enhance performance, often accomplished by combining probability distributions. However, previous approaches face the challenge of high computational costs for training multiple models. Furthermore, for […]
Adversarial Latent-State Training for Robust Policies in Partially Observable Domains
arXiv:2603.07313v1 Announce Type: cross Abstract: Robustness under latent distribution shift remains challenging in partially observable reinforcement learning. We formalize a focused setting where an adversary selects a hidden initial latent distribution before the episode, termed an adversarial latent-initial-state POMDP. Theoretically, we prove a latent minimax principle, characterize worst-case defender distributions, and derive approximate best-response certificates […]
Reinforcing the World’s Edge: A Continual Learning Problem in the Multi-Agent-World Boundary
arXiv:2603.06813v1 Announce Type: new Abstract: Reusable decision structure survives across episodes in reinforcement learning, but this depends on how the agent–world boundary is drawn. In stationary, finite-horizon MDPs, an invariant core: the (not-necessarily contiguous) subsequences of state–action pairs shared by all successful trajectories (optionally under a simple abstraction) can be constructed. Under mild goal-conditioned assumptions, […]
ConfHit: Conformal Generative Design with Oracle Free Guarantees
arXiv:2603.07371v1 Announce Type: cross Abstract: The success of deep generative models in scientific discovery requires not only the ability to generate novel candidates but also reliable guarantees that these candidates indeed satisfy desired properties. Recent conformal-prediction methods offer a path to such guarantees, but its application to generative modeling in drug discovery is limited by […]