Large language models struggle with reasoning over long contexts where relevant information is sparsely distributed.Although planand-execute frameworks mitigate this by decomposing tasks into planning and execution, their effectiveness is often limited by unreliable plan generation due to dependence on surfacelevel cues.Consequently, plans may be based on incorrect assumptions, and once a plan is formed, identifying errors and revising it reliably becomes difficult, limiting the effectiveness of reactive refinement.To address this limitation, we propose PPA-Plan, a proactive planning strategy for long-context reasoning that focuses on preventing such failures before plan generation.PPA-Plan identifies potential logical pitfalls and false assumptions, formulates them as negative constraints, and conditions plan generation on explicitly avoiding these constraints.Experiments on long-context QA benchmarks show that executing plans generated by PPA-Plan consistently outperforms existing plan-and-execute methods and direct prompting.Query: How many times did Burnett operate the claw in the passage?1. burnett_claw_operations = FIND_ELEMENT(CTX, "operate", "Burnett claw") 2. count_operations = COUNT_X(CTX, burnett_claw_operations) Answer: He directs the claw to retrieve Kriere from space.Then, he operates the claw to close around Kriere and crush him.So, the number of times Burnett operates the claw is 2. Query: Why was the murderer trying to kill Bo?⚠ Negative Constraints: -Assuming the motive for the murder is explicitly stated, rather than needing to infer it from context clues or character interactions.-Assuming that the murderer has a single, clear motive, overlooking the possibility of multiple, complex reasons for their actions.Plan ① FIND_RELATION ② FIND_ELEMENT ③ FIND_EVENT ④ CONCAT 1. murderer_relationship = FIND_RELATION(CTX,"murderer", "Bo") 2. motive_clues = FIND_ELEMENT(CTX, "motive clues", "murderer") 3. past_events = FIND_EVENT(CTX, "murderer") 4. combined_motive = CONCAT(murderer_relationship, motive_clues, past_events) Answer: …(details omitted).While the text does not provide explicit details about the murderer's motives, it suggests a broader context of violence and rivalry among individuals in the asteroid belt.Example (a) Planner Plan ① FIND_ELEMENT ② COUNT_X Executor Result 1 Result 2Query: Why was the murderer trying to kill Bo? 1. murderer_intent = FIND_INTENT(CTX, "murderer") 2. bo_relationship = FIND_RELATION(CTX, "Bo", "murderer") 3. motive = FIND_ELEMENT(CTX, "motive", "murderer") 4. ans = CONCAT(murderer_intent, bo_relationship, motive) Planner Executor

This paper details the development of a Shared Indoor Smart Mobility device called AngGo. As a precursor to the development process, we conducted user research on three kinds of outdoor personal mobility. Our goal was to determine the major differences between outdoor and indoor personal mobility and to ensure that AngGo would meet the requirements of indoor personal mobility in a practical way, as informed by the results of surveys and interviews. Tests were conducted on the time-of-flight sensors to be used for indoor autonomous driving. Manual mode as well as the experiment-based equations governing the sensors were optimized through user testing. Our observational experiments, which were carried out in the lobby of a building, showed that both autonomous and manual modes functioned as designed. This study makes a contribution to the literature by describing how our AngGo device features an autonomous driving platform that can transport riders around an indoor environment.

This poster presents an efficient secure scan design based on a fake key to protect a secret key from scan-based side channel attack. This technique targeted for an SoC embedding an Advanced Encryption Standard (AES) core can be adopted without requiring any modification to the functional body of the IP core, thus overheads for area, timing, and power are negligible while preserving the compatibility with the IEEE1149.1 standard.