An Evaluation Agent, middleware that combines Natural Language Inference factual verification, a five-signal poison detector with relevance-weighted aggregation, and a Trust Index is proposed, which reliably blocks instruction injection of unsafe advice while contradiction and subtle semantic weakening remain hard.
Balkrishna Giri, M. Hasan, Jussi Rasku et al.· 0 citations
This work deconstructs the RL post-training algorithm, investigating each step to clarify what is actually happening beneath the surface, and uses the entropy of the policy's output distribution as a lens to compare the distributions learned through pretraining, SFT, and RL post-training, revealing how each stage shapes model certainty.
D. Clay, Saket Gollapudi, Sankar V Harilal et al.· 0 citations
CyberFactory is introduced, a unified open-source framework that connects data construction, trajectory synthesis, and model training across proof-of-concept (PoC) generation, vulnerability patching, and cybersecurity question answering (CyberQA).
Jian Yang, Haau-Sing Li, Shawn Guo et al.· 0 citations
The overall results show that editable, consolidated memory can supply remembered context for robot planning, and full MEMORA--combining editing, typed stores, and consolidation--achieves the strongest aggregate results among the evaluated memory conditions.
Zihao Yu, Xiu Yuan, Chongjie Zhang· 0 citations
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This work introduces a general steering technique called Semantic Overlays: small learned adapters applied at chosen prefill positions to a frozen model's residual stream that defends against the broad class of prompt injections that add instructions in untrusted context.
Dual-Layer Agentic Memory is proposed, a framework that shifts memory management to the write phase through cost-aware epistemic routing and periodic parametric consolidation, allowing the router to adaptively suppress redundant writes as the model's epistemic boundaries evolve.
Wenzhi Li, Dong Nie, Ruiyi Lan et al.· 0 citations
SyRuP is introduced, a decoding-time framework for improving system-prompt adherence while keeping the base LM frozen, and results suggest that explicit token-level guidance is an effective and practical mechanism for reliable system-prompt following.
Seoyeon Kim, Minjae Kang, Jaehyung Kim· 0 citations
It is shown that mechanistic diagnosis can serve as a practical guide for targeted adaptation in underrepresented-language medical LLMs, and Targeted Low-Rank Adaptation (TLoRA) is proposed, restricted to the layer window where cross-lingual representations diverge, upstream of the output layers where the failure manifests.
Chaimae Abouzahir, Musa Khan, Hala Ali-Hassan et al.· 0 citations
An automated TCS research framework that generates, formalizes, filters, and proves new claims, and further develops an automated TCS research framework that generates, formalizes, filters, and proves new claims.
Dingzirui Wang, Xuanliang Zhang, Keyan Xu et al.· 0 citations
These findings show that reliable evaluation of LLM-generated code requires validated ground truth, protected tests, and multiple explicitly interpreted measures, and that CodeAssay provides a reproducible basis for evidence-based model evaluation in AI-augmented software development.
Shahbaz Siddeeq, Muhammad Waseem, Umar Subhan Malhi et al.· 0 citations
A novel committor learning framework grounded in the AlphaFold 3 paradigm is proposed that elucidates how ligand substituents regulate the ratio between distinct binding pathways, offering new perspectives for structure-based drug design.
Jintu Zhang, Zichang Jin, Huifeng Zhao et al.· 0 citations
MemDefrag, a training-free and model-agnostic framework that uses a middle-layer tracing signal to conduct memory defragmentation (rank, reorder, and filter memories), and applies an informativeness-guided proportional forgetting mechanism once capacity is exceeded, is proposed.
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
MIT News · Artificial Intelligence· news.mit.eduAug 31, 2026
With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
New MIT research could lead to better materials for a fossil-fuel-free process for making the chemical that's essential to fertilizer and other products.
