Large language models (LLMs) can generate fluent outputs that lack source support or conflict with local operational constraints. We introduce Self-Reflective Chain-of-Thought (SRCoT), a modular architecture that organizes generation as a closed Retrieve--Reflect--Revise loop. SRCoT separates environment state, persist...
Fabian Broszio, Michael Hoffmann, Adrian Paschke· 0 citations
A key challenge in on-policy distillation (OPD) is extracting effective supervision from teacher knowledge. Supervision from token probabilities may underutilize internal knowledge, while direct hidden-state matching forces the student to shift to the teacher's feature space and complicates cross-architecture transfer....
Kai Tang, Xing Zheng, Haobo Wang et al.· 0 citations
Annotrace measures agreement between any two annotators, human or AI, and reads six trace formats, including OpenTelemetry GenAI, LangSmith and Langfuse, and turns each run into an ordered, nested list of steps.
Arun Josephraj Arokiaraj, Chen Huang, Bin Wu et al.· 0 citations
Large Language Models (LLMs) have demonstrated remarkable success in complex reasoning through explicit Chain-of-Thought (CoT), but the associated computational overhead and token verbosity remain significant barriers to efficiency. While implicit reasoning in continuous latent spaces offers a promising alternative, ex...
Yin-Long Xu, Jian-Feng Huang, Zheng-Dong Zhou et al.· 0 citations
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Language-model agents are increasingly deployed to act on behalf of people in settings where individual decisions shape shared outcomes. We examine whether five production models adapt their contributions to changing risks and partner behaviour, or instead follow a relatively fixed strategy. In a collective-risk dilemm...
Trung-Kiet Huynh, Dao-Sy Duy-Minh, Chi-Nguyen Tran et al.· 0 citations
Text-based world models have recently shown potential to simulate complex RL environment state transitions for a diverse set of use cases. However, no standardized benchmark exists to compare, study and improve these models. Through this paper, we attempt to solve this problem by building GlobeBench, a novel benchmark...
Nicholas Saban, Charvi Bannur, Mariya Vasileva et al.· 0 citations
Modern mobile GUI agents can perceive visible screens and execute local actions, but they still lack persistent app-specific navigation knowledge: where functions live, which pages contain them, and which screens are equivalent across repeated visits. We introduce TAG-Nav, a Task-Agnostic Graph Navigation framework tha...
Adamo Orsini, Seyed Mehdi Ayyoubzadeh, Sadman Rakib Pinon et al.· 0 citations
Large language models (LLMs) are increasingly used as formalizers: given a reasoning problem expressed in natural language, they produce a formal specification that a symbolic solver can then solve, with guarantees relative to that specification. Many specifications are runnable yet semantically wrong, encoding incorre...
G. Vaccarino, Emanuele La Malfa, De-Hai Min et al.· 0 citations
Large reasoning models (LRMs) that leverage test-time computation achieve strong performance on mathematical and scientific benchmarks and are increasingly deployed in real-world applications. However, their ability to perform \textit{everyday logical reasoning}, which often departs from textbook formal logic, remains...
Kentaro Ozeki, Risako Ando, Takanobu Morishita et al.· 0 citations
LLM agents are used to check records against an organization's requirements, for example whether a vulnerability report is ready for investigation. Requirements often allow several ways to be met, such as a severity rating with a curator-reviewed advisory or a published advisory link. Agents fail by accepting a report...
LLMs can generate Lean specifications from task descriptions, but those descriptions are often underspecified or ambiguous and may omit verifier-relevant edge cases, yielding imperfect formal specifications. We study whether an elicitation agent (EA) can improve verifier-checked Lean specifications by seeking clarifica...
Mowafak Allaham, Aditya Vempaty, Ashish Jagmohan et al.· 0 citations
Experiments show that structured behaviors outperform monolithic prompt optimization, while feedback-driven evolution and moderation further improve effectiveness and reduce downside risk, suggesting that agentic memory evolution supports interpretable and transferable product optimization.
Hyunseo Kim, Suil Choe, Suk-Ho So et al.· 0 citations
