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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 · agent543 papers50 this week
agent collaboration · multi agent · based multi agent224 papers36 this week
teacher policy distillation · policy distillation opd · teacher supervision369 papers30 this week
scientific agents · research agents · ai agents208 papers28 this week
conversational agents · user simulators · user simulation182 papers24 this week
learning verifiable rewards · reinforcement learning verifiable · reinforcement learning rl169 papers22 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

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Recent papers

All papers in Research

arXiv cs.AIOct 25
MultiEvoSkill: Structure-Aware Multi-Agent Skill Evolution for Generalizable Skill Artifacts
Kounianhua Du, Jianxing Liu, Ruoshui Shan, Yucheng Zhang, Ke Li +2
Skill documents are becoming the default adaptation surface for frozen large language model (LLM) agents, but current skill generators face two key limitations. First, humans and different LLMs differ in how they organize procedural knowledge and approach task solving. Skills authored from a human or single-model perspective may therefore fail to align with the reasoning behaviors of other backbones, limiting cross-model generalization. Second, sequential skill descriptions inadequately represent procedures with parallel branches and explicit dependencies, while existing skill optimizers primarily operate on prose and lack explicit support for graph-structured skill evolution. We introduce MultiEvoSkill, a skill-optimization framework comprising three core components: 1) Skill compiler casts the skill as a typed graph with tailored operators, producing a compact, informative representation with explicit procedural topology; 2) Heterogeneous agent system proposes revision advices using collective intelligence, where minimal and informative issues are identified based on multiple sets of experiences. 3) Structure-aware debate and evolution mechanism organizes candidate edits into a…
PDF arXiv
arXiv cs.LGOct 25
Alignment via Generalization
Shai Shalev-Shwartz, Amnon Shashua
AI systems can become better at achieving the goals we give them without becoming better at doing what we actually want. Studying this alignment problem rigorously requires a framework connecting measurable performance to intended behavior. We propose Aligned-PAC learning, whose central idea is to reduce alignment to generalization: does success on the requirements used for training extend to requirements the system has not encountered? Rather than express our entire intention through a single training objective, we represent its different aspects through a collection of specific requirements. This perspective already yields strong theoretical results. We show how independent evaluation can provide rigorous evidence of alignment across the represented requirements, and establish how much evaluation is needed. We also develop an optimization method that, under explicit assumptions, combines learned models to satisfy the training requirements. The framework does not solve alignment in full: choosing requirements that faithfully capture our intentions, and understanding when learning will generalize to unseen requirements, remain open challenges. It provides a foundation for making…
PDF arXiv
arXiv cs.CLCodeOct 25▲ 28
$Δτ$-Bench: Pushing Agents Toward a Moving Capability
Shuofei Qiao, Xuanguang Pan, Yijun Chen, Wendi Feng, Bin Wu +4
In this paper, we introduce $Δτ$-Bench, an executable agent benchmark that models the structural evolution of large tool ecosystems. In $Δτ$-Bench, independently evolving services continually reshape how tasks can be accomplished: familiar tools may disappear or be replaced, while more efficient alternatives emerge amid overlapping functionality and noisy documentation. Against this moving capability frontier, we evaluate whether agents can proactively discover efficient tool-use paths and promptly revise their accumulated procedural knowledge, bringing together demands that existing benchmarks fail to examine. $Δτ$-Bench comprises 1,588 validated multi-turn tasks across 42 domains and six successive ecosystem states generated through capability-preserving transformations. Our evaluation shows that even the strongest evaluated model or harness achieves only 42.3% task success, while initial gains from existing skill-learning methods can not consistently translate into better adaptation as the ecosystem evolves. These findings motivate Steward, an ecosystem manager that jointly manages tools and procedural knowledge through incremental skill maintenance and dynamic provisioning…
PDF arXiv Code
arXiv cs.MAOct 25
The Coordination Tax and the Heterogeneity Dividend: Break-Even Conditions for Multi-Agent LLM Pipelines
Andreas Zettl, Diego Gosmar
Multi-agent LLM pipelines built on interoperability protocols such as the Open Floor Protocol (OFP) or A2A are usually justified on capability grounds, with their runtime cost treated as an overhead of unknown size. Comparing such a pipeline with one model call is underspecified, because a pipeline can give each role a different model. We give a closed-form model that sets the coordination tax (decomposition overhead plus per-handoff protocol cost) against the heterogeneity dividend from routing work to a cheaper model, with a break-even threshold and a feasibility bound, $α< 1/r$, and measure every term, with OFP as the protocol under test, on 135 HumanEval and ClassEval tasks in 2430 runs of six architectures with Claude Opus 5.5 and Haiku 5.5. The protocol adds 7.2 ms per handoff, 0.11% of pipeline wall time. Decomposition does not pay on these tasks: a planner-coder-reviewer pipeline costs 3.22x as much as a single strong call, takes 3.24x as long, and solves no more tasks. Moving planner and reviewer to the cheap model cuts the pipeline's cost to 0.39x but changes pass@1 by -6.7 points, leaving it dearer, slower and less accurate than the single call, and the break-even…
PDF arXiv
arXiv cs.AIOct 25
MatOS: An Agent Operating System for Materials Research
Dingyang Lyu, Jing Gao, Zhonghao Chang, Jingjie Huang, Xiangran Cheng +11
Materials research relies on scattered and growing data, knowledge, and simulation tools, and a single study often spans multi-day workflows across literature analysis, computation, and experiment. Large language model agents can now retrieve materials data, operate simulation codes, and close design-synthesis-characterization loops with autonomous laboratories. However, extending these agents with new capabilities, keeping long-running tasks on track, and letting them learn from past tasks and user feedback remain difficult. Here we present MatOS, an agent operating system for materials research with three core components: (1) a layered scientific asset graph that organizes data, knowledge, tools, skills, roles, and workflows as reusable assets; (2) a stateful adaptive workflow that runs tasks as explicit execution graphs with scientific checks, so that they can be resumed and traced; and (3) evidence-driven asset evolution, which revises these assets from execution records and user feedback. On ResearchClawBench, MatOS improves on the previously best-performing agent by 11.15% with the same base model and extends the cost-score Pareto frontier. With a common base model, it…
PDF arXiv
arXiv cs.LGOct 25
Knowledge distillation across teacher architectures for compact diabetic retinopathy grading
Mossa Manisa, Habiba Jahan Priya, A. K. M. Fakhrul Hossain
Diabetic retinopathy grading from retinal fundus photographs is important but difficult to deploy on resource-constrained hardware. We conducted a controlled study of offline response-based knowledge distillation for five-class diabetic retinopathy grading on APTOS 2019, comparing ResNet-50, MobileNetV3-Large, and Swin Transformer-Tiny (Swin-T) teachers distilled into a MobileNetV3-Small student. A 3 x 4 x 2 grid of teacher, temperature, and hard-label weight was screened at one seed, while six configurations and the no-distillation baseline were evaluated across three seeds. The highest observed single-seed quadratic weighted kappa was 0.9093 (macro-F1 0.6851) for Swin-T at temperature 4 and hard-label weight 0.7; the corresponding three-seed mean was 0.9024, compared with 0.8804 for the baseline. The student used 1.52 M parameters and 5.94 MB, representing an 18.07x parameter reduction relative to Swin-T. Temperature and hard-label weight effects were teacher- and configuration-dependent, while small performance differences were sensitive to random seed. Knowledge distillation produced a compact student with competitive performance under the evaluated protocol, but independent…
PDF arXiv

Papers

Topics in this field

recursive self improvement · ai · agent543 papers50 this week
agent collaboration · multi agent · based multi agent224 papers36 this week
teacher policy distillation · policy distillation opd · teacher supervision369 papers30 this week
scientific agents · research agents · ai agents208 papers28 this week
conversational agents · user simulators · user simulation182 papers24 this week
learning verifiable rewards · reinforcement learning verifiable · reinforcement learning rl169 papers22 this week

Active contributors · 90 days

Recent papers

All papers in Research

arXiv cs.AIOct 25
MultiEvoSkill: Structure-Aware Multi-Agent Skill Evolution for Generalizable Skill Artifacts
Kounianhua Du, Jianxing Liu, Ruoshui Shan, Yucheng Zhang, Ke Li +2
Skill documents are becoming the default adaptation surface for frozen large language model (LLM) agents, but current skill generators face two key limitations. First, humans and different LLMs differ in how they organize procedural knowledge and approach task solving. Skills authored from a human or single-model perspective may therefore fail to align with the reasoning behaviors of other backbones, limiting cross-model generalization. Second, sequential skill descriptions inadequately represent procedures with parallel branches and explicit dependencies, while existing skill optimizers primarily operate on prose and lack explicit support for graph-structured skill evolution. We introduce MultiEvoSkill, a skill-optimization framework comprising three core components: 1) Skill compiler casts the skill as a typed graph with tailored operators, producing a compact, informative representation with explicit procedural topology; 2) Heterogeneous agent system proposes revision advices using collective intelligence, where minimal and informative issues are identified based on multiple sets of experiences. 3) Structure-aware debate and evolution mechanism organizes candidate edits into a…
PDF arXiv
arXiv cs.LGOct 25
Alignment via Generalization
Shai Shalev-Shwartz, Amnon Shashua
AI systems can become better at achieving the goals we give them without becoming better at doing what we actually want. Studying this alignment problem rigorously requires a framework connecting measurable performance to intended behavior. We propose Aligned-PAC learning, whose central idea is to reduce alignment to generalization: does success on the requirements used for training extend to requirements the system has not encountered? Rather than express our entire intention through a single training objective, we represent its different aspects through a collection of specific requirements. This perspective already yields strong theoretical results. We show how independent evaluation can provide rigorous evidence of alignment across the represented requirements, and establish how much evaluation is needed. We also develop an optimization method that, under explicit assumptions, combines learned models to satisfy the training requirements. The framework does not solve alignment in full: choosing requirements that faithfully capture our intentions, and understanding when learning will generalize to unseen requirements, remain open challenges. It provides a foundation for making…
PDF arXiv
arXiv cs.CLCodeOct 25▲ 28
$Δτ$-Bench: Pushing Agents Toward a Moving Capability
Shuofei Qiao, Xuanguang Pan, Yijun Chen, Wendi Feng, Bin Wu +4
In this paper, we introduce $Δτ$-Bench, an executable agent benchmark that models the structural evolution of large tool ecosystems. In $Δτ$-Bench, independently evolving services continually reshape how tasks can be accomplished: familiar tools may disappear or be replaced, while more efficient alternatives emerge amid overlapping functionality and noisy documentation. Against this moving capability frontier, we evaluate whether agents can proactively discover efficient tool-use paths and promptly revise their accumulated procedural knowledge, bringing together demands that existing benchmarks fail to examine. $Δτ$-Bench comprises 1,588 validated multi-turn tasks across 42 domains and six successive ecosystem states generated through capability-preserving transformations. Our evaluation shows that even the strongest evaluated model or harness achieves only 42.3% task success, while initial gains from existing skill-learning methods can not consistently translate into better adaptation as the ecosystem evolves. These findings motivate Steward, an ecosystem manager that jointly manages tools and procedural knowledge through incremental skill maintenance and dynamic provisioning…
PDF arXiv Code
arXiv cs.MAOct 25
The Coordination Tax and the Heterogeneity Dividend: Break-Even Conditions for Multi-Agent LLM Pipelines
Andreas Zettl, Diego Gosmar
Multi-agent LLM pipelines built on interoperability protocols such as the Open Floor Protocol (OFP) or A2A are usually justified on capability grounds, with their runtime cost treated as an overhead of unknown size. Comparing such a pipeline with one model call is underspecified, because a pipeline can give each role a different model. We give a closed-form model that sets the coordination tax (decomposition overhead plus per-handoff protocol cost) against the heterogeneity dividend from routing work to a cheaper model, with a break-even threshold and a feasibility bound, $α< 1/r$, and measure every term, with OFP as the protocol under test, on 135 HumanEval and ClassEval tasks in 2430 runs of six architectures with Claude Opus 5.5 and Haiku 5.5. The protocol adds 7.2 ms per handoff, 0.11% of pipeline wall time. Decomposition does not pay on these tasks: a planner-coder-reviewer pipeline costs 3.22x as much as a single strong call, takes 3.24x as long, and solves no more tasks. Moving planner and reviewer to the cheap model cuts the pipeline's cost to 0.39x but changes pass@1 by -6.7 points, leaving it dearer, slower and less accurate than the single call, and the break-even…
PDF arXiv
arXiv cs.AIOct 25
MatOS: An Agent Operating System for Materials Research
Dingyang Lyu, Jing Gao, Zhonghao Chang, Jingjie Huang, Xiangran Cheng +11
Materials research relies on scattered and growing data, knowledge, and simulation tools, and a single study often spans multi-day workflows across literature analysis, computation, and experiment. Large language model agents can now retrieve materials data, operate simulation codes, and close design-synthesis-characterization loops with autonomous laboratories. However, extending these agents with new capabilities, keeping long-running tasks on track, and letting them learn from past tasks and user feedback remain difficult. Here we present MatOS, an agent operating system for materials research with three core components: (1) a layered scientific asset graph that organizes data, knowledge, tools, skills, roles, and workflows as reusable assets; (2) a stateful adaptive workflow that runs tasks as explicit execution graphs with scientific checks, so that they can be resumed and traced; and (3) evidence-driven asset evolution, which revises these assets from execution records and user feedback. On ResearchClawBench, MatOS improves on the previously best-performing agent by 11.15% with the same base model and extends the cost-score Pareto frontier. With a common base model, it…
PDF arXiv
arXiv cs.LGOct 25
Knowledge distillation across teacher architectures for compact diabetic retinopathy grading
Mossa Manisa, Habiba Jahan Priya, A. K. M. Fakhrul Hossain
Diabetic retinopathy grading from retinal fundus photographs is important but difficult to deploy on resource-constrained hardware. We conducted a controlled study of offline response-based knowledge distillation for five-class diabetic retinopathy grading on APTOS 2019, comparing ResNet-50, MobileNetV3-Large, and Swin Transformer-Tiny (Swin-T) teachers distilled into a MobileNetV3-Small student. A 3 x 4 x 2 grid of teacher, temperature, and hard-label weight was screened at one seed, while six configurations and the no-distillation baseline were evaluated across three seeds. The highest observed single-seed quadratic weighted kappa was 0.9093 (macro-F1 0.6851) for Swin-T at temperature 4 and hard-label weight 0.7; the corresponding three-seed mean was 0.9024, compared with 0.8804 for the baseline. The student used 1.52 M parameters and 5.94 MB, representing an 18.07x parameter reduction relative to Swin-T. Temperature and hard-label weight effects were teacher- and configuration-dependent, while small performance differences were sensitive to random seed. Knowledge distillation produced a compact student with competitive performance under the evaluated protocol, but independent…
PDF arXiv

Activity · 12 weeks

Fields · AI · updated 12m ago

Recursive self improvement

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

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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.