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9 papers from Amazon Science on Reasoning & Chain-of-Thought
Memory-augmented LLMs get a strategic upgrade: MemMA uses multi-agent reasoning to proactively guide memory construction and repair, leading to significant performance gains.
LLM reasoning research is inadvertently paving a dangerous path towards AI situational awareness and strategic deception, demanding a re-evaluation of current safety measures.
Save 20% on LLM costs with <2% accuracy drop by strategically cascading a small model with a large one, guided by a confidence-calibrated SLM.
LLMs can ace math problems while reasoning like a drunk toddler, with 82% of correct answers arising from unstable, inconsistent logic.
Latent reasoning models often take shortcuts to achieve high accuracy, and stronger supervision, while mitigating this, paradoxically restricts the diversity of their latent representations.
Static benchmarks can be fooled by fluent text and aligned citations, but DREAM leverages agentic evaluation to expose the critical capability mismatch in assessing temporal validity and factual correctness of research agents.
Forget prompt engineering and fine-tuning: this "Reasoning Inception" method injects targeted reasoning into LLM agents at test time to fix conversational errors on the fly.
General-purpose Causal Foundation Models can now match the performance of specialized causal models by incorporating partial causal graph information via attention bias, unlocking a more unified approach to causal inference.
Achieve near 20-point accuracy gains in reasoning tasks by dynamically routing between latent and discrete reasoning spaces based on model confidence.
