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Open problems in Recursive self improvement

Gradient conflicts in multi-teacher distillation1 paper raises it · 1 addresses it
Conflicting teacher signals degrade joint optimization and cause capability loss when training a single student from multiple teachers.
- Beyond Domain-Routed Supervision: Cross-Teacher Guarding for Multi-Teacher On-Policy Distillationraises · addresses
Unreliable supervision in cross-tokenizer distillation1 paper raises it · 1 addresses it
Vocabulary mismatch and alignment coverage issues make teacher feedback noisy or harmful for student training.
- Rethinking Cross-Tokenizer On-Policy Distillation: From Alignment Coverage to Supervision Reliabilityraises · addresses
Inconsistent policy shifts during teacher training1 paper raises it · 1 addresses it
Endpoint policy shifts may hide early and late preference changes that cancel out, leading to poor transfer.
- Waypoint-OPD: Transferring RL-Induced Policy Shifts via Gated On-Policy Distillationraises · addresses
Lack of decision-grounded trajectory supervision1 paper raises it · 1 addresses it
Successful trajectories often include actions unsupported by the agent's visible history, reducing learning effectiveness.
- Trajectory2Gym: Decision-Grounded Compositional Interaction Synthesis for Tool-Use Agentsraises · addresses
Credit assignment in long-horizon agent training1 paper raises it · 1 addresses it
Critic values vary across turns, making fine-grained credit allocation and policy updates difficult.
- Multiscale Policy Optimization for Long-Horizon LLM Agentsraises · addresses

Named by an AI from these papers' abstracts.

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Topics in this field

recursive self improvement · ai · agent520 papers38 this week
teacher policy distillation · policy distillation opd · teacher supervision354 papers20 this week
multi agent debate · ai agents · agent debate231 papers19 this week
agent collaboration · multi agent · based multi agent206 papers18 this week
scientific agents · research agents · ai agents196 papers18 this week
conversational agents · user simulators · user simulation173 papers18 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

Activity

Recent papers

All papers in Research

arXiv cs.MAOct 27
Cooperative EV Charging Assignment Using Multi-Agent Reinforcement Learning: A Comparison of QMIX and Independent Q-Learning Under Urban Traffic Uncertainty
Nour-Eddine Moumni, Rachid Alaoui, Driss Kiouach
The allocation of electric vehicles to public charging stations is increasingly constrained by the fact that several heterogeneous facilities often serve the same stream of requests while observing only partial local information. An accept/reject decision that is defensible for one station may nevertheless reduce the efficiency of the charging district as a whole. We formulate this operational problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) and examine whether Centralized Training with Decentralized Execution (CTDE), instantiated through QMIX, improves assignment performance relative to a cooperative Independent Q-Learning baseline (IQL). The evaluation uses a six-station urban network simulated with SUMO, five independent random seeds, 200 training episodes, and four traffic flux scenarios ranging from 100 to 250 vehicles/hour. Under this protocol, QMIX reaches a mean reward per evaluated decision of +0.776, versus +0.578 for IQL (delta = +0.198, seed-paired Cohens dz = 2.98, exact two-sided Wilcoxon p = 0.0625, the minimum attainable value at this sample size), and raises the acceptance rate from 72.4% to 94.0% (delta = +21.6 pp, dz = 5.24,…
PDF arXiv
arXiv cs.SEOct 27
Reusable LLM-Based Plans with Probabilistic Guarantees for Self-Adaptive Systems
Asher Tempest Arya, Rajan Maghera, Nicholas Wu Jiang, Federico Mora, Aren A. Babikian +1
Self-adaptive systems are designed to accommodate variants of their environment, which form a family of instances whose uncertainty is captured by probabilistic models, and must meet probabilistic requirements on each instance. Within such a family, changes in size and structure complicate the fundamental adaptation decision: whether to retain, reuse, or construct a new controller, since controllers are often synthesized for and specific to an instance. Large language models (LLMs) have recently been used to write reusable controllers, but existing approaches largely target deterministic domains, and probabilistic model checking of their output remains limited to preliminary single-instance studies. In this paper, we propose a verifier-guided framework for controller reuse with explicit support for families of environments. The framework introduces a novel combination of LLM-written controllers with probabilistic model checking to provide per-instance guarantees for probabilistic requirements. We evaluate our approach using three LLMs on three benchmarks, assessing its effectiveness and cost with respect to baseline implementations, the distribution of adaptation decisions, and…
PDF arXiv
arXiv cs.CVOct 27
You Don't Need Aligned Representations: Knowledge Distillation via Random Prototype Spaces
Hamza Etcibasi, Ramazan Gokberk Cinbis
A widely held assumption in knowledge distillation is that teacher and student representations must occupy a compatible or explicitly aligned feature space. We propose RPKD (Random Prototype Knowledge Distillation), which discards this assumption by comparing both networks against a shared bank of randomly initialized, frozen prototype vectors. Alignment is therefore carried out in an external reference space rather than in the teacher's native feature coordinates: the teacher side is projected by fixed random maps that carry no trainable parameters, and only the student adapts. Two branches handle the transfer: a logit branch aligning global prototype similarity distributions and a feature branch matching spatially-resolved prototype activation maps. Both operate within the same fixed prototype space, which can be interpreted as a random feature embedding that approximately preserves inter-sample similarity structure, so the prototype bank and the distillation objective stay unchanged across teacher-student pairs while only the input projections are instantiated per architecture. In low-category settings, decoupling the prototype vocabulary from the task label space yields…
PDF arXiv
arXiv cs.AIOct 27
GARDEN: Generalist Autonomous Reward Design Through Evidence Navigation for Cooperative Multi-Agent Reinforcement Learning
Dogan Urgun
Cooperative multi-agent reinforcement learning (MARL) enables teams of agents, such as robots and autonomous vehicles, to learn coordinated behavior, but what such teams learn is largely determined by their reward function. Because task success alone provides too little guidance for learning, intermediate rewards are usually designed by hand, and this effort must often be repeated whenever the environment or the team changes. Large language models (LLMs) can automate the effort by writing reward functions as code, yet the resulting rewards are typically designed and tested in a single setting. To enable their reuse across settings, GARDEN (Generalist Autonomous Reward Design through Evidence Navigation) is proposed, in which one reward program is developed for a family of related scenarios and transferred, unchanged, to scenarios that were not available during its design. During development, environment code, training records, and agent behavior are investigated by an LLM reward engineer, and draft rewards are tested in trial training runs, while the learning algorithm and the rule for selecting the final reward are held fixed. On unseen scenarios of the Overcooked and SMAX…
PDF arXiv
arXiv cs.AIOct 27
VerifiedLab: Harnessing Knowns for Unknowns
Yu Yao, Jiayi Dong, Yang Yang, Shuiwang Ji, Ju Li
Across human history, knowledge has undergone several transitions, from orality to writing and then to digital archives. At each transition, humans established new conventions to preserve knowledge for the new era. Nowadays, as research agents are increasingly deployed for laboratory experiments, knowledge is crossing another boundary: from records written for humans to those for machines. In this work, we introduce VerifiedLab, a multi-agent harness framework for this human-to-machine transition of knowledge. VerifiedLab transforms research output from passive artifacts into active systems that accelerate downstream use, adoption and prediction of future trends. In VerifiedLab, a team of AI scientists with distinct roles reasons over records verified field by field: a principal investigator plans, a librarian computes the evidence directly from the records, specialists draft, and a gate and a critic check each claim against the measured reliability of its data before the harness decides whether to release or withhold it. We demonstrate the efficacy of VerifiedLab through extensive experiments on perovskite solar cells: VerifiedLab recovers known physical trends, anticipates…
PDF arXiv
arXiv cs.AICodeOct 27▲ 1
SciGame: Let Agents Play the Scientific Discovery Games in the Real and Counterfactual World
Yusheng Zhao, Bowei He, Xue Liu, Mingxiao Li
Large language model agents are increasingly used for scientific discovery. However, existing benchmarks mix law inference with programming, score data fitting instead of prediction on unseen inputs, and do not separate empirical evidence from pretrained priors. We introduce SciGame, a controlled benchmark that converts scientific literature into structured scientific cards. Each card yields a pair of Original and dimensionally consistent Counterfactual environments with the same task interface. SciGame contains a Historical collection of canonical laws and a live Temporal collection curated from papers published after model knowledge cutoffs. It evaluates agents in candidate-guided Identification and open-ended Discovery settings. Each setting runs under Prior-only, Fixed, and Adaptive protocols. Experiments on five frontier models yield three findings. First, familiar priors hurt performance once the governing relation is altered. Second, adaptive experimentation helps the strongest models but harms weaker ones under limited budgets. Third, good explanations can hide quantitative failures. Recovering the exact symbolic form remains the main bottleneck, even when agents…
PDF arXiv Code

Papers

Topics in this field

recursive self improvement · ai · agent520 papers38 this week
teacher policy distillation · policy distillation opd · teacher supervision354 papers20 this week
multi agent debate · ai agents · agent debate231 papers19 this week
agent collaboration · multi agent · based multi agent206 papers18 this week
scientific agents · research agents · ai agents196 papers18 this week
conversational agents · user simulators · user simulation173 papers18 this week

Active contributors · 90 days

Activity · 12 weeks

Recent papers

All papers in Research

arXiv cs.MAOct 27
Cooperative EV Charging Assignment Using Multi-Agent Reinforcement Learning: A Comparison of QMIX and Independent Q-Learning Under Urban Traffic Uncertainty
Nour-Eddine Moumni, Rachid Alaoui, Driss Kiouach
The allocation of electric vehicles to public charging stations is increasingly constrained by the fact that several heterogeneous facilities often serve the same stream of requests while observing only partial local information. An accept/reject decision that is defensible for one station may nevertheless reduce the efficiency of the charging district as a whole. We formulate this operational problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) and examine whether Centralized Training with Decentralized Execution (CTDE), instantiated through QMIX, improves assignment performance relative to a cooperative Independent Q-Learning baseline (IQL). The evaluation uses a six-station urban network simulated with SUMO, five independent random seeds, 200 training episodes, and four traffic flux scenarios ranging from 100 to 250 vehicles/hour. Under this protocol, QMIX reaches a mean reward per evaluated decision of +0.776, versus +0.578 for IQL (delta = +0.198, seed-paired Cohens dz = 2.98, exact two-sided Wilcoxon p = 0.0625, the minimum attainable value at this sample size), and raises the acceptance rate from 72.4% to 94.0% (delta = +21.6 pp, dz = 5.24,…
PDF arXiv
arXiv cs.SEOct 27
Reusable LLM-Based Plans with Probabilistic Guarantees for Self-Adaptive Systems
Asher Tempest Arya, Rajan Maghera, Nicholas Wu Jiang, Federico Mora, Aren A. Babikian +1
Self-adaptive systems are designed to accommodate variants of their environment, which form a family of instances whose uncertainty is captured by probabilistic models, and must meet probabilistic requirements on each instance. Within such a family, changes in size and structure complicate the fundamental adaptation decision: whether to retain, reuse, or construct a new controller, since controllers are often synthesized for and specific to an instance. Large language models (LLMs) have recently been used to write reusable controllers, but existing approaches largely target deterministic domains, and probabilistic model checking of their output remains limited to preliminary single-instance studies. In this paper, we propose a verifier-guided framework for controller reuse with explicit support for families of environments. The framework introduces a novel combination of LLM-written controllers with probabilistic model checking to provide per-instance guarantees for probabilistic requirements. We evaluate our approach using three LLMs on three benchmarks, assessing its effectiveness and cost with respect to baseline implementations, the distribution of adaptation decisions, and…
PDF arXiv
arXiv cs.CVOct 27
You Don't Need Aligned Representations: Knowledge Distillation via Random Prototype Spaces
Hamza Etcibasi, Ramazan Gokberk Cinbis
A widely held assumption in knowledge distillation is that teacher and student representations must occupy a compatible or explicitly aligned feature space. We propose RPKD (Random Prototype Knowledge Distillation), which discards this assumption by comparing both networks against a shared bank of randomly initialized, frozen prototype vectors. Alignment is therefore carried out in an external reference space rather than in the teacher's native feature coordinates: the teacher side is projected by fixed random maps that carry no trainable parameters, and only the student adapts. Two branches handle the transfer: a logit branch aligning global prototype similarity distributions and a feature branch matching spatially-resolved prototype activation maps. Both operate within the same fixed prototype space, which can be interpreted as a random feature embedding that approximately preserves inter-sample similarity structure, so the prototype bank and the distillation objective stay unchanged across teacher-student pairs while only the input projections are instantiated per architecture. In low-category settings, decoupling the prototype vocabulary from the task label space yields…
PDF arXiv
arXiv cs.AIOct 27
GARDEN: Generalist Autonomous Reward Design Through Evidence Navigation for Cooperative Multi-Agent Reinforcement Learning
Dogan Urgun
Cooperative multi-agent reinforcement learning (MARL) enables teams of agents, such as robots and autonomous vehicles, to learn coordinated behavior, but what such teams learn is largely determined by their reward function. Because task success alone provides too little guidance for learning, intermediate rewards are usually designed by hand, and this effort must often be repeated whenever the environment or the team changes. Large language models (LLMs) can automate the effort by writing reward functions as code, yet the resulting rewards are typically designed and tested in a single setting. To enable their reuse across settings, GARDEN (Generalist Autonomous Reward Design through Evidence Navigation) is proposed, in which one reward program is developed for a family of related scenarios and transferred, unchanged, to scenarios that were not available during its design. During development, environment code, training records, and agent behavior are investigated by an LLM reward engineer, and draft rewards are tested in trial training runs, while the learning algorithm and the rule for selecting the final reward are held fixed. On unseen scenarios of the Overcooked and SMAX…
PDF arXiv
arXiv cs.AIOct 27
VerifiedLab: Harnessing Knowns for Unknowns
Yu Yao, Jiayi Dong, Yang Yang, Shuiwang Ji, Ju Li
Across human history, knowledge has undergone several transitions, from orality to writing and then to digital archives. At each transition, humans established new conventions to preserve knowledge for the new era. Nowadays, as research agents are increasingly deployed for laboratory experiments, knowledge is crossing another boundary: from records written for humans to those for machines. In this work, we introduce VerifiedLab, a multi-agent harness framework for this human-to-machine transition of knowledge. VerifiedLab transforms research output from passive artifacts into active systems that accelerate downstream use, adoption and prediction of future trends. In VerifiedLab, a team of AI scientists with distinct roles reasons over records verified field by field: a principal investigator plans, a librarian computes the evidence directly from the records, specialists draft, and a gate and a critic check each claim against the measured reliability of its data before the harness decides whether to release or withhold it. We demonstrate the efficacy of VerifiedLab through extensive experiments on perovskite solar cells: VerifiedLab recovers known physical trends, anticipates…
PDF arXiv
arXiv cs.AICodeOct 27▲ 1
SciGame: Let Agents Play the Scientific Discovery Games in the Real and Counterfactual World
Yusheng Zhao, Bowei He, Xue Liu, Mingxiao Li
Large language model agents are increasingly used for scientific discovery. However, existing benchmarks mix law inference with programming, score data fitting instead of prediction on unseen inputs, and do not separate empirical evidence from pretrained priors. We introduce SciGame, a controlled benchmark that converts scientific literature into structured scientific cards. Each card yields a pair of Original and dimensionally consistent Counterfactual environments with the same task interface. SciGame contains a Historical collection of canonical laws and a live Temporal collection curated from papers published after model knowledge cutoffs. It evaluates agents in candidate-guided Identification and open-ended Discovery settings. Each setting runs under Prior-only, Fixed, and Adaptive protocols. Experiments on five frontier models yield three findings. First, familiar priors hurt performance once the governing relation is altered. Second, adaptive experimentation helps the strongest models but harms weaker ones under limited budgets. Third, good explanations can hide quantitative failures. Recovering the exact symbolic form remains the main bottleneck, even when agents…
PDF arXiv Code

Open problems in Recursive self improvement

Gradient conflicts in multi-teacher distillation1 paper raises it · 1 addresses it
Conflicting teacher signals degrade joint optimization and cause capability loss when training a single student from multiple teachers.
- Beyond Domain-Routed Supervision: Cross-Teacher Guarding for Multi-Teacher On-Policy Distillationraises · addresses
Unreliable supervision in cross-tokenizer distillation1 paper raises it · 1 addresses it
Vocabulary mismatch and alignment coverage issues make teacher feedback noisy or harmful for student training.
- Rethinking Cross-Tokenizer On-Policy Distillation: From Alignment Coverage to Supervision Reliabilityraises · addresses
Inconsistent policy shifts during teacher training1 paper raises it · 1 addresses it
Endpoint policy shifts may hide early and late preference changes that cancel out, leading to poor transfer.
- Waypoint-OPD: Transferring RL-Induced Policy Shifts via Gated On-Policy Distillationraises · addresses
Lack of decision-grounded trajectory supervision1 paper raises it · 1 addresses it
Successful trajectories often include actions unsupported by the agent's visible history, reducing learning effectiveness.
- Trajectory2Gym: Decision-Grounded Compositional Interaction Synthesis for Tool-Use Agentsraises · addresses
Credit assignment in long-horizon agent training1 paper raises it · 1 addresses it
Critic values vary across turns, making fine-grained credit allocation and policy updates difficult.
- Multiscale Policy Optimization for Long-Horizon LLM Agentsraises · addresses

Named by an AI from these papers' abstracts.

Fields · AI · updated 1h ago

Recursive self improvement

Found by the topic model: recursive self improvement, teacher policy distillation, multi agent debate.

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