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Multimodal reasoning stands as a pivotal capability for large vision-language models (LVLMs). The integration with Domain-Specific Languages (DSL), offering precise visual representations, equips these models with the opportunity to execute more accurate reasoning in complex and professional domains. However, the vanilla Chain-of-Thought (CoT) prompting method faces challenges in effectively leveraging the unique strengths of visual and DSL representations, primarily due to their differing reasoning mechanisms. Additionally, it often falls short in addressing critical steps in multi-step reasoning tasks. To mitigate these challenges, we introduce the \underline{B}i-Modal \underline{B}ehavioral \underline{A}lignment (BBA) prompting method, designed to maximize the potential of DSL in augmenting complex multi-modal reasoning tasks. This method initiates by guiding LVLMs to create separate reasoning chains for visual and DSL representations. Subsequently, it aligns these chains by addressing any inconsistencies, thus achieving a cohesive integration of behaviors from different modalities. Our experiments demonstrate that BBA substantially improves the performance of GPT-4V(ision) on geometry problem solving ($28.34\% \to 34.22\%$), chess positional advantage prediction ($42.08\% \to 46.99\%$) and molecular property prediction ($77.47\% \to 83.52\%$).

A young computer scientist who grew up in Mississippi is focusing her efforts on fairness and identifying biases in technology. Though she's working in Atlanta as an artificial intelligence researcher for Amazon, she's reinvesting much of her earnings towards the development of a multi-million dollar innovation center that's set to transform her native downtown Jackson. Dr. Nashlie Sephus is also CEO of The Bean Path -- a non-profit that works to bridge the "tech gap" in communities where access to technical expertise, computer coding and other resources are limited. She speaks candidly with host Eddie Robinson about her experiences in closing commercial real estate deals in the Deep South and how she's worked in a field of study where there's not many Black women with PhDs.

In response to this need, Shelby Lyman, the host of past Public Broadcasting Station (PBS) series on world chess championship matches, organized the AGS Challenge Match at the New School for Social Research in New York City. Funding for this event was provided by AGS Computers, Inc., a New Jersey-based software firm. The match was held September 22-25, with one game played each day, and was widely covered by the international press. Participating were Hitech, at 2407 then the highest-rated computer in the world, and International Grandmaster Arnold S. Denker, a former U.S. champion. Denker's rating of 2410 was comparable to that of Hitech.

I] presented a systematic account of the facts of thought and of the methods to express them. But if we do not restrict the domain, there are too many things to be considered. Brunot accurately studied various ways to express surprise, prices, anteriority in the future, a question... But concepts used only in specific domains do not appear in his book. That is why I chose a technical domain -- annotations of chess games -- and I tried to fmd what was said in that domain.

It creates some plans and tries to execute them. It analyses the situations deeper in the tree only if the plan fails. In that case it generates new plans correcting what is wrong in the old one. So, the program considers only natural branches of the tree. It can find combinations for which it is necessary to look more than twenty ply ahead. The paper describes the methods used for analyzing a situation and for modifying unsuccessful plans. Then we examine some results found by the program.