textarcana · October 10, 2025 13:33 · Oct 10, 2025 · Oct 10, 2025
diff --git a/Prompt_chaining_across_model_families.Md b/Prompt_chaining_across_model_families.Md
@@ -43,7 +43,7 @@ This document consolidates highly cited foundational papers and their citing wor
 
 ---
 
-## Reference Links
+
 
 [ai-chains]: https://dl.acm.org/doi/abs/10.1145/3491102.3517582  
 [prompt-stepwise]: https://arxiv.org/pdf/2406.00507  

diff --git a/Prompt_chaining_across_model_families.Md b/Prompt_chaining_across_model_families.Md
@@ -0,0 +1,60 @@
+# Cross-Model Prompt Chaining: Expanded and Filtered Literature Review
+
+This document consolidates highly cited foundational papers and their citing works relevant to **cross-model prompt chaining** across different LLM families (e.g., GPT, Claude, Qwen). Each entry includes a link to its source.
+
+---
+
+## Highly-cited seed papers (≥ 5 citations)
+
+1. [**AI Chains (CHI’22)**][ai-chains] — formalizes prompt chaining; tooling makes swapping steps/models straightforward.  
+2. [**Prompt Chaining vs Stepwise (Findings ACL’24)**][prompt-stepwise] — chaining empirically outperforms single long prompts; supports staged flows that can be mapped onto different models.  
+3. [**Mixture-of-Agents (ICLR’25)**][moa] — layered ensembles of different LLMs; strong heterogeneous results.  
+4. [**Exchange-of-Thought (EMNLP’23)**][eot] — explicit cross-model communication (Memory / Report / Relay / Debate) to pass reasoning between models.  
+5. [**FrugalGPT (TMLR / ICLR’24)**][frugalgpt] — routing / cascades select among multiple LLM APIs per query (router + scorer + stop-judger).
+
+---
+
+## New citing papers relevant to cross-model chaining
+
+- [**Rethinking Mixture-of-Agents (2025)**][rethinking-moa] — evaluates when heterogeneous mixing (different families) helps vs a “Self-MoA” using only the single best model; decision insights are directly useful for GPT→Claude→Qwen handoffs. (Cites MoA.)  
+
+- [**Deep Research Agents (2025)**][deep-research] — surveys agent systems that blend multiple model families in pipelines (e.g., GPT-4.x, Claude-Sonnet, Gemini, DeepSeek); practical cross-model orchestration patterns. (Cites MoA and related multi-agent work.)  
+
+- [**When Two LLMs Debate (2025)**][llm-debate] — analyzes inter-model debate dynamics and confidence revision; applicable as a handoff stage where models critique each other’s code/patches. (Cites / extends debate-style cross-model interaction lines that also reference EoT.)  
+
+- [**From Standalone LLMs to Integrated Intelligence (CAIS Survey 2025)**][integrated-intel] — taxonomy of orchestration strategies (components / roles / routing) for multi-model systems; design references for chained, cross-family pipelines. (Surveys and cites routing / ensemble literature incl. MoA / FrugalGPT-style methods.)  
+
+- [**Knowledge-Empowered, Collaborative, and Co-Evolving LLMs (2024)**][knowledge-collab] — focuses on model collaboration and co-evolution, covering mechanisms to combine different LLMs / tools; relevant for deciding what role each family plays in a chain.  
+
+- [**Human Intervention in LLM Multi-Agent Debate (2024)**][human-intervention] — studies human-in-the-loop control in multi-agent (often cross-model) debate pipelines; helpful guardrails for cross-model code handoffs. (Cites multi-agent debate lines related to EoT-style setups.)  
+
+- [**ChainBuddy (2024)**][chainbuddy] — assistant that generates evaluative LLM pipelines in ChainForge; supports planning / evaluating multi-step chains where models can be swapped — useful for implementing cross-family stage assignments. (Builds atop prompt-chaining HCI work such as AI Chains.)  
+
+- [**Advances & Open Problems for LLMs (2025 Survey)**][advances-llm] — synthesizes evidence around MoA and heterogeneous teaming; extracts conditions where mixing different models is beneficial, informing when to escalate across families.
+
+---
+
+## How to use these for GPT→Claude→Qwen handoffs
+
+- **[Design the chain][ai-chains]** with AI Chains / ChainBuddy patterns; assign roles per family (e.g., GPT for drafting / spec-aware scaffolds, Claude for safety / compliance critique, Qwen for refactor / optimization).  
+- **[Add routing / cascades][frugalgpt]** to escalate to stronger / more expensive families only if a cheap pass (e.g., Qwen-small) or an automated scorer flags low quality / uncertainty.  
+- **[Enable cross-model reasoning transfer (EoT)][eot]**: pass not just code but rationales / diffs / tests between models; optionally add a short debate round before merging.  
+- **[Sanity-check mixing][rethinking-moa]** with MoA + Rethinking-MoA insights: in some contexts, a single strong model with self-aggregation can beat mixing; measure before committing to heavy cross-family ensembles.
+
+---
+
+## Reference Links
+
+[ai-chains]: https://dl.acm.org/doi/abs/10.1145/3491102.3517582  
+[prompt-stepwise]: https://arxiv.org/pdf/2406.00507  
+[moa]: https://arxiv.org/abs/2406.04692  
+[eot]: https://arxiv.org/abs/2312.01823  
+[frugalgpt]: https://arxiv.org/abs/2305.05176  
+[rethinking-moa]: https://arxiv.org/abs/2501.00064  
+[deep-research]: https://arxiv.org/abs/2503.10007  
+[llm-debate]: https://arxiv.org/abs/2504.02888  
+[integrated-intel]: https://arxiv.org/abs/2502.00643  
+[knowledge-collab]: https://arxiv.org/abs/2407.05619  
+[human-intervention]: https://arxiv.org/abs/2410.09077  
+[chainbuddy]: https://arxiv.org/abs/2403.18417  
+[advances-llm]: https://arxiv.org/abs/2503.02401