5 papers
The Crowded Embedding Space: A Mean-Field Mechanism for Emergent Marginalization in Retrieval-Augmented Agents
Shwan Ashrafi, Dan Roth
Retrieval-augmented generative agents rely on retrieval for grounding, yet are typically evaluated on a query-by-query basis. This isolates interactions that are geometrically coup…
Formalize, Don't Optimize: The Heuristic Trap in LLM-Generated Combinatorial Solvers
Haoyu Wang, Yuliang Song, Tao Li +5
Large Language Models (LLMs) struggle to solve complex combinatorial problems through direct reasoning, so recent neuro-symbolic systems increasingly use them to synthesize executa…
Budget-Aware Anytime Reasoning with LLM-Synthesized Preference Data
Xuanming Zhang, Shwan Ashrafi, Aziza Mirsaidova +5
We study the reasoning behavior of large language models (LLMs) under limited computation budgets. In such settings, producing useful partial solutions quickly is often more practi…
Tree-based Dialogue Reinforced Policy Optimization for Red-Teaming Attacks
Ruohao Guo, Afshin Oroojlooy, Roshan Sridhar +3
Despite recent rapid progress in AI safety, current large language models remain vulnerable to adversarial attacks in multi-turn interaction settings, where attackers strategically…
AI+HW 2035: Shaping the Next Decade
Deming Chen, Jason Cong, Azalia Mirhoseini +27
Artificial intelligence (AI) and hardware (HW) are advancing at unprecedented rates, yet their trajectories have become inseparably intertwined. The global research community lacks…