Learning GenAI via SOTA Papers

Title: LLM-as-Code Agentic Programming for Agent Harness Source: http://arxiv.org/abs/2606.15874v1 Summary: This paper proposes a fundamental architectural shift by replacing probabilistic LLM-based orchestration with deterministic 'Agentic Programming' where code governs the control flow. This framework eliminates control-flow hallucinations and token explosion, establishing a robust new...

Title: T-Mem: Memory That Anticipates, Not Archives Source: http://arxiv.org/abs/2606.15405v1 Summary: T-Mem presents a foundational shift in agentic memory by transitioning from passive similarity-based retrieval to an anticipatory 'episodic future thinking' architecture. By using write-time rehearsal triggers, it enables agents to recall information across latent semantic arcs, effective...

Title: CoAgent: Concurrency Control for Multi-Agent Systems Source: http://arxiv.org/abs/2606.15376v1 Summary: This paper introduces CoAgent, a novel concurrency control framework that leverages LLM reasoning to resolve conflicts in multi-agent systems without the bottlenecks of traditional locking or retries. It establishes the MTPO protocol as a foundational primitive for high-concurrency agenti...

Title: No Accidental Software Agent First Canonical Code for Human Code Entropy Reduction and 30 to 500 times Lower Frontier Model Requirements Source: http://arxiv.org/abs/2606.14357v1 Summary: This paper proposes a 'proof-carrying substrate' that canonicalizes software behavior to drastically reduce the complexity and model capacity required for autonomous coding. It introduces a novel a...

Title: Closing the Reflection Gap: A Free Calibration Bonus for Agentic RL Source: http://arxiv.org/abs/2606.14211v1 Summary: It introduces RefGRPO, a novel reinforcement learning algorithm that utilizes environment feedback to calibrate an agent's self-reflection without additional reward models. This framework creates a grounded reasoning loop that allows agents to serve as their own verifie...

Title: Reward Modeling for Multi-Agent Orchestration Source: http://arxiv.org/abs/2606.13598v1 Summary: This paper presents OrchRM, a self-supervised framework that enables the training of multi-agent orchestrators without human annotations, achieving a 10x improvement in token efficiency. It establishes orchestration-level reward modeling as a scalable and foundational approach for coordinating s...

Title: Agents-K1: Towards Agent-native Knowledge Orchestration Source: http://arxiv.org/abs/2606.13669v1 Summary: This work introduces a novel end-to-end pipeline for converting raw scientific documents into 'agent-native' knowledge graphs, moving beyond simple abstract-based retrieval to enable complex scientific reasoning. By establishing a unifying theoretical foundation for multimodal...

Title: Ouroboros-Spatial: Closing the Data-Model Loop for Spatial Reasoning Source: http://arxiv.org/abs/2606.11719v1 Summary: This paper introduces a self-evolving training framework that enables models to co-evolve their training distribution with their own capabilities by acting as both proposer and solver. It represents a significant breakthrough in efficiency and reasoning by closing the data...

Title: LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems Summary: This paper defines the architectural principles for next-generation graph-native AI systems by synergizing LLMs with structured graph computation for grounded, multi-hop reasoning. It establishes a unified framework for LLM-augmented retrieval, knowledge graph integration, and graph-algorithm-strengthened agentic planning. So...

Title: A Five-Plane Reference Architecture for Runtime Governance of Production AI Agents Source: http://arxiv.org/abs/2606.12320v1 Summary: This paper proposes a formal Five-Plane Reference Architecture for agent governance, defining critical architectural primitives like reasoning planes and composite principals for autonomous systems. It provides the foundational structural framework necessary...

Title: The Power of Test-Time Training for Approximate Sampling Source: http://arxiv.org/abs/2606.11437v1 Summary: This work provides a foundational theoretical framework for Test-Time Training (TTT) by formalizing it as an approximate sampling problem and establishing query complexity lower bounds. It offers principled mathematical backing for the emerging paradigm of adapting model weights at in...

Title: Role-Agent: Bootstrapping LLM Agents via Dual-Role Evolution Source: http://arxiv.org/abs/2606.10917v1 Summary: The Role-Agent framework introduces a dual-role evolution cycle where an LLM functions as both agent and environment to bootstrap its own reasoning capabilities. This novel agentic framework addresses the limitations of static training data by enabling self-correcting co-evolution...

Title: ReasonAlloc: Hierarchical Decoding-Time KV Cache Budget Allocation for Reasoning Models Source: http://arxiv.org/abs/2606.11164v1 Summary: ReasonAlloc introduces a hierarchical KV cache allocation strategy that significantly optimizes memory usage during the long chain-of-thought trajectories characteristic of modern reasoning models. By identifying "Reasoning Wave" demand pattern...

Title: Experience Makes Skillful: Enabling Generalizable Medical Agent Reasoning via Self-Evolving Skill Memory Source: http://arxiv.org/abs/2606.09365v1 Summary: This paper proposes SkeMex, a foundational architecture for self-evolving agent memory that enables the distillation and governance of procedural skills from interaction trajectories. It introduces a novel "Read-Write-Assess-Govern&...

Title: End-to-End Context Compression at Scale Source: http://arxiv.org/abs/2606.09659v1 Summary: This paper introduces Latent Context Language Models (LCLMs), a novel architectural primitive that utilizes encoder-decoder compression to efficiently handle long-context sequences at scale. It establishes a new Pareto frontier for accuracy and efficiency, providing a foundational backbone for next-ge...

Title: Capability-Aligned Hierarchical Learning for Tool-Augmented LLMs Source: http://arxiv.org/abs/2606.09371v1 Summary: This paper proposes Capability-Aligned Hierarchical Learning (CAHL), a novel framework that jointly optimizes high-level planning and low-level execution policies using reinforcement learning. It addresses the fundamental bottleneck of planner-executor misalignment, creating a...

Title: Bittensor Agent Arenas as a Trajectory Primitive: Distilling a Shopping Agent from ShoppingBench Subnet Traces Source: http://arxiv.org/abs/2606.10064v1 Summary: This paper introduces the concept of Agent Arenas as a "trajectory primitive," establishing a novel framework for generating diverse, incentive-aligned training data for agentic post-training. This approach represents a s...

Title: When RL Fails after SFT: Rejuvenating Model Plasticity for Robust SFT-to-RL Handoff Source: http://arxiv.org/abs/2606.09932v1 Summary: This paper identifies and solves the critical 'loss of plasticity' bottleneck in the standard LLM post-training pipeline where excessive SFT inhibits subsequent RL optimization. It introduces 'Rejuvenation', a foundational training primitive...

Title: Trajectory-Refined Distillation Source: http://arxiv.org/abs/2606.08432v1 Summary: This paper identifies and mitigates 'prefix failure' in on-policy distillation, a structural issue that hampers the efficiency of reasoning-scale post-training. By introducing trajectory-level corrections, it provides a foundational efficiency breakthrough that improves exploration and reasoning accur...

Title: ConMem: Structured Memory-Guided Adaptation in Training-Free Multi-Agent Systems Source: http://arxiv.org/abs/2606.08702v1 Summary: ConMem establishes a novel framework for multi-agent adaptation using relation-aware memory graphs to distill and coordinate reusable strategies from historical trajectories. It represents a foundational advancement in agentic reasoning loops by enabling robust...

Title: VATS: Exploiting Implicit Authority in Error-Path Injection via Systematic Mutation Source: http://arxiv.org/abs/2606.07992v1 Summary: This study exposes a foundational vulnerability in agentic reasoning by identifying 'implicit authority' within error-handling loops as a primary vector for bypassing safety heuristics. It provides a critical analysis of the Model Context Protocol (M...

Title: Semantic Quorum Assurance: Collective Certification for Non-Deterministic AI Infrastructure Source: http://arxiv.org/abs/2606.08021v1 Summary: This paper establishes Semantic Quorum Assurance (SQA) as a new architectural primitive for the reliable governance of non-deterministic agentic infrastructure. It introduces a multi-agent consensus framework that shifts focus from deterministic stat...

Title: ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning Source: http://arxiv.org/abs/2606.06915v1 Summary: This paper introduces a unified framework for test-time compute scaling, a critical paradigm that allows LLMs to improve reasoning by allocating more compute during inference. It provides a modular library and benchmark to standardize and optimize quality-cost...

Title: Socratic-SWE: Self-Evolving Coding Agents via Trace-Derived Agent Skills Source: http://arxiv.org/abs/2606.07412v1 Summary: This work presents a closed-loop self-evolution framework where software agents learn by distilling their own historical solving traces into structured skills. This approach enables agents to autonomously generate and solve a targeted curriculum of tasks, significantly...

Title: TokenMizer: Graph-Structured Session Memory for Long-Horizon LLM Context Management Source: http://arxiv.org/abs/2606.06337v1 Summary: TokenMizer introduces a graph-structured architectural primitive for managing long-horizon session memory, replacing inefficient flat-text history with a typed knowledge graph. This system achieves significant token compression while preserving the structura...