PERFECT REASONING FOR EVERY AI AGENT, EXPLAINED with CODE.
Symbolic Reasoning for all LLM based Agents to boost reasoning performance.
ReasonAgain aims to enhance the evaluation of large language models' (LLMs) mathematical reasoning by employing symbolic programs instead of relying solely on final answer accuracy. The methodology involves transforming existing mathematical questions into Python-based symbolic programs that encapsulate the underlying reasoning logic. By generating new input-output pairs through parameter perturbations, ReasonAgain tests whether LLMs can consistently apply correct reasoning across different variations. This approach provides a dynamic evaluation that highlights LLM fragility, revealing inconsistencies that static, final-answer-based metrics may overlook.
In experiments, ReasonAgain demonstrated a significant decline in performance when LLMs were exposed to perturbed versions of questions compared to static datasets. This outcome emphasizes the current limitations of LLMs, suggesting that they often depend on superficial heuristics rather than deeply understanding the reasoning processes. By systematically surfacing these weaknesses, ReasonAgain points toward pathways for improving the robustness of LLM reasoning.
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ReasonAgain: Using Extractable Symbolic Programs to Evaluate
Mathematical Reasoning
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GitHub repo:
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00:00 LLM fail in logic reasoning
01:48 Symbolic Code representation
03:35 Symbolic perturbations
04:50 20 percent LLM accuracy
07:12 My Logic Test Symbolic encoded
11:25 Prolog Code for logic test
11:43 LISP, Haskell, CLIPS, SCALA code
13:18 NEW Reasoning power for AI Systems
17:00 AI Agent Reasoning enhanced
18:42 ReasonAgain paper Microsoft AMD
21:18 Limitations
22:47 No Prompt Engineering required
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