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RECON: An LLM-Enhanced Backward Constraint Analysis Framework

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arXiv:2606. 10264v1 Announce Type: new Abstract: While traditional techniques, such as symbolic execution, provide a principled foundation for precise constraint reasoning in program analysis, they struggle to scale to modern software systems mainly due to path explosion, the need for function modeling, and the loss of semantic intent at low-level program representations.

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Authors: Babangida Bappah, Lamine Noureddine, Umar Farooq, Aisha Ali-Gombe
Released: 2026-06-10 (29d ago)
Topic: Related to current project
Relation to my work: Read next because RECON: An LLM-Enhanced Backward Constraint Analysis Framework overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, title, under, soft, eval, rate, extraction, compare. Source: arxiv cs.CR (Cryptography and Security).
arXiv: 2606.10264

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