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#smt

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Calliefan

Oh yeah, should also mention the Demon Summoner: Raidou Kuzunoha remaster they also announced on the Nintendo Direct. This was in my secret wishlist, and it's coming this summer!

Limited Run are handling the physical release, so I might have to consider preordering that.

youtube.com/watch?v=wrbHRMm0tx

YouTubeRAIDOU Remastered: The Mystery of the Soulless Army — Announcement TrailerBy Official ATLUS West
#raidou#smt#NintendoDirect
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Benjamin Carr, Ph.D. 👨🏻‍💻🧬

SMT Remains Very Advantageous For #Zen5 #AMD #EPYC Performance
#SMT typically was of measurable benefit to the 5th Gen AMD EPYC processor with the exception of some #HPC workloads that perform better with SMT disabled or otherwise limited by memory bandwidth. SMT also hurt the OpenVINO inference latency but by and large Simultaneous Multi-Threading remains an important and valuable feature for AMD processors.
phoronix.com/review/amd-epyc-z

www.phoronix.comSMT Remains Very Advantageous For 5th Gen AMD EPYC Performance Review
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Games Japan

wacoca.com/games/1092168/ 【D2メガテン】ついに原作コラボでヒーローとヒロイン登場！これはまさにぶっ壊れ性能？【女神転生】【雑談】【SMT】 #BGM #Ｄ×２真・女神転生リベレーション【RPG】 #D2 #MobileGames #MobileRPGGames #RPGアプリ #smt #アップデート #オススメ悪魔 #コラボ #スマホゲームアプリ #デュエル #ヒーロー #ヒロイン #メガテン #女神転生 #実況 #最強 #真1 #雑談

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José A. Alonso

NL2FOL: Translating natural language to first-order logic for logical fallacy detection. ~ Abhinav Lalwani et als. arxiv.org/abs/2405.02318 #LLMs #Logig #SMT

arXiv.orgNL2FOL: Translating Natural Language to First-Order Logic for Logical Fallacy DetectionTranslating natural language into formal language such as First-Order Logic (FOL) is a foundational challenge in NLP with wide-ranging applications in automated reasoning, misinformation tracking, and knowledge validation. In this paper, we introduce Natural Language to First-Order Logic (NL2FOL), a framework to autoformalize natural language to FOL step by step using Large Language Models (LLMs). Our approach addresses key challenges in this translation process, including the integration of implicit background knowledge. By leveraging structured representations generated by NL2FOL, we use Satisfiability Modulo Theory (SMT) solvers to reason about the logical validity of natural language statements. We present logical fallacy detection as a case study to evaluate the efficacy of NL2FOL. Being neurosymbolic, our approach also provides interpretable insights into the reasoning process and demonstrates robustness without requiring model fine-tuning or labeled training data. Our framework achieves strong performance on multiple datasets. On the LOGIC dataset, NL2FOL achieves an F1-score of 78%, while generalizing effectively to the LOGICCLIMATE dataset with an F1-score of 80%.
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José A. Alonso

Logic.py: Bridging the gap between LLMs and constraint solvers. ~ Pascal Kesseli, Peter O'Hearn, Ricardo Silveira Cabral. arxiv.org/abs/2502.15776 #LLMs #Logic #SAT #SMT

arXiv.orgLogic.py: Bridging the Gap between LLMs and Constraint SolversWe present a novel approach to formalise and solve search-based problems using large language models, which significantly improves upon previous state-of-the-art results. We demonstrate the efficacy of this approach on the logic puzzles benchmark ZebraLogicBench. Instead of letting the LLM attempt to directly solve the puzzles, our method prompts the model to formalise the problem in a logic-focused domain-specific language (DSL) called Logic.py. This formalised representation is then solved using a constraint solver, leveraging the strengths of both the language model and the solver. Our approach achieves a remarkable 65% absolute improvement over the baseline performance of Llama 3.1 70B on ZebraLogicBench, setting a new state-of-the-art with an accuracy of over 90%. This significant advancement demonstrates the potential of combining language models with domain-specific languages and auxiliary tools on traditionally challenging tasks for LLMs.
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