[SPEC] Truth Engine translation layer — NL to formal language mapping #13

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perplexity · 2026-03-27T22:48:37Z

perplexity commented

2026-03-27 22:48:37 +00:00

Source

"Integrating Symbolic Reasoning Engines into Hermes Agent", Sections 1.1.3 and 1.2.1.

What

Spec the translation layer that converts natural language queries into formal specifications for symbolic engines.

Key challenges (from paper)

Ambiguity resolution: natural language is vague, formal languages are precise
Progressive formalization: try simpler encodings before more expressive ones
Feedback loops: solver errors guide formalization refinement
Latency vs. correctness tradeoff: adaptive timeout management, difficulty estimation

Translation approaches to document

Few-shot prompting with exemplar formalizations
Learned semantic parsers trained on parallel corpora (NL ↔ SMT-LIB)
Interactive clarification protocols for ambiguous queries
Iterative refinement: LLM generates formal code → solver rejects → LLM revises

Eval contract

Each translation attempt should output: {formal_code, engine, confidence, formalization_attempts, solver_result}
Failed translations logged for training data

Acceptance

specs/truth-engine-translation-layer.md documenting the architecture, approaches, and eval contract

## Source "Integrating Symbolic Reasoning Engines into Hermes Agent", Sections 1.1.3 and 1.2.1. ## What Spec the translation layer that converts natural language queries into formal specifications for symbolic engines. ### Key challenges (from paper) - Ambiguity resolution: natural language is vague, formal languages are precise - Progressive formalization: try simpler encodings before more expressive ones - Feedback loops: solver errors guide formalization refinement - Latency vs. correctness tradeoff: adaptive timeout management, difficulty estimation ### Translation approaches to document 1. Few-shot prompting with exemplar formalizations 2. Learned semantic parsers trained on parallel corpora (NL ↔ SMT-LIB) 3. Interactive clarification protocols for ambiguous queries 4. Iterative refinement: LLM generates formal code → solver rejects → LLM revises ### Eval contract - Each translation attempt should output: `{formal_code, engine, confidence, formalization_attempts, solver_result}` - Failed translations logged for training data ## Acceptance - `specs/truth-engine-translation-layer.md` documenting the architecture, approaches, and eval contract

perplexity referenced this issue

2026-03-28 00:42:11 +00:00

[RESEARCH] OpenClaw — architecture spike and integration plan #19

perplexity referenced this issue

2026-03-28 00:58:24 +00:00

Perplexity Knowledge Transfer — full session synthesis filtered through #542 #22

Timmy was assigned by Rockachopa

2026-03-28 03:52:32 +00:00

Timmy commented

2026-03-28 04:45:36 +00:00

Closing as duplicate during backlog burn-down. Canonical issue: #11.

Reason: this workstream already exists with materially the same title/scope. Keeping one canonical thread prevents agent churn and review waste.

Closing as duplicate during backlog burn-down. Canonical issue: #11. Reason: this workstream already exists with materially the same title/scope. Keeping one canonical thread prevents agent churn and review waste.

Timmy closed this issue

2026-03-28 04:45:36 +00:00

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Reference: Timmy_Foundation/timmy-home#13