OpenRath: Session-Centered Runtime State for Agent Systems

• URL: http://arxiv.org/abs/2606.19409v1
• Date: Wed, 17 Jun 2026 15:19:19 GMT
• Title: OpenRath: Session-Centered Runtime State for Agent Systems
• Abstract summary: OpenRath is a PyTorch-like programming model for multi-agent, multi-session systems.<n>Session is branchable, inspectable, replayable, backend-aware, and composable.<n>Report presents the programming model, architecture, audited milestones, and evidence protocol.
• Score: 2.9568259440852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern agent systems often suffer from fragmented runtime state: transcripts, tool effects, memory events, workspace placement, branch provenance, and replay evidence are recorded separately and become difficult to inspect or reproduce. OpenRath addresses this issue with a PyTorch-like programming model for multi-agent, multi-session systems. The analogy concerns the role of a central first-class runtime abstraction, not tensor computation. Its core abstraction is Session, the runtime value passed between agents and workflows. A Session is branchable, inspectable, replayable, backend-aware, and composable. It records conversation chunks, sandbox placement, lineage metadata, token usage, pending work, and tool evidence, while defining where memory interactions enter the runtime record. Since this state is carried by the same value used in program execution, fork, merge, and replay become explicit runtime operations rather than states reconstructed from external traces. OpenRath further defines Sandbox, Tool, Agent, Memory, Workflow, and Selector, with Selector turning control flow into runtime-routed decisions. This report presents the programming model, architecture, audited milestones, and evidence protocol. Its claims are limited to controlled runtime properties, while broad quantitative comparisons, live-provider quality, optional-backend availability, and memory quality are left for follow-on evaluation. The central thesis is that Session provides agent systems with a first-class runtime value for auditable composition.

Related papers

• EchoPath: Execution-Level Replayable Memory for GUI Agents [45.101663136838916]
  We introduce EchoPath, a model-agnostic harness that converts artifact-validated GUI trajectories into standardized, parameter-controlled callable memories.<n>Each memory stores task-intent keys, application and state preconditions, flexible input parameters, GUI evidence, validation provenance, and lifecycle state.<n>In experiments with real computer-use tasks, EchoPath reduced median token cost by more than 90% and median execution time by about 60%.
  arXiv  Detail & Related papers  (2026-09-15T04:56:17Z)
• Context as an Environment: Programmatic Context Management for Long-Horizon Agents [47.21335299084925]
  We present Scroll, a context manager that treats each agent session as an executable Session Environment.<n>The kernel maintains a typed across model calls, allowing tool outputs, retrieved history, and derived state to be bound to variables.
  arXiv  Detail & Related papers  (2026-08-21T23:39:19Z)
• Tencent WorkBuddy Bench: A Multi-Domain Coding-Agent Benchmark with Contamination-Resistant Task Construction [57.138342889101345]
  Tencent WorkBuddy Bench is a multi-domain evaluation suite for coding agents.<n>This report documents its construction methodology, scoring protocol, and a cross-model leaderboard.
  arXiv  Detail & Related papers  (2026-07-23T04:34:06Z)
• Retriever: Composing Closed-Loop Asynchronous Robot Programs [30.120352978306574]
  Retriever is a graph of stateful causal stream functions executed on explicit run clocks.<n>It compiles these streams into a runtime that supports multiple backends.<n>We evaluate Retriever through a real-robot case study.
  arXiv  Detail & Related papers  (2026-07-19T12:14:28Z)
• PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution [49.776611937968]
  We present PhyAgentOS, a foundation delivering scheduling, verification, memory, benchmarking, and safety as system-level services.<n>Its Session-Centered treats a session, not an action, as the minimum unit of scheduling, compatibility, supervised execution, evidence collection, and acceptance.<n>SessionVerifier distinguishes execution termination from semantic task completion via evidence-grounded verdicts of success, failure, or replan.<n> Benchmarking reuses the deployment session and verification path, so results trace to real execution.
  arXiv  Detail & Related papers  (2026-07-18T04:46:53Z)
• PACMS: Submodular Context Selection as a Pluggable Engine for LLM Agents [0.9134244356393664]
  LLM agents operate over a context window that fills from several directions simultaneously.<n>The prevailing mechanism is recency truncation, sometimes paired with periodic summarization.<n>Retrieval augmented generation fetches external documents into the prompt but does not arbitrate the agent's emphalready-present pooled context.
  arXiv  Detail & Related papers  (2026-06-18T10:22:57Z)
• Cordon: Semantic Transactions for Tool-Using LLM Agents [11.567293065381918]
  This paper introduces Cordon, a transactional runtime system for staging and validating agent effects before commit.<n>It reduces irreversible-effect failures while preserving benign task completion with modest approval and latency overhead.
  arXiv  Detail & Related papers  (2026-06-16T06:21:14Z)
• Agent libOS: A Library-OS-Inspired Runtime for Long-Running, Capability-Controlled LLM Agents [0.6345523830122165]
  Large language model (LLM) agents are evolving from request-response assistants into long-running software actors.<n>This paper presents Agent libOS, a library-OS-inspired runtime substrate for LLM agents.
  arXiv  Detail & Related papers  (2026-06-02T16:53:24Z)
• Mem-W: Latent Memory-Native GUI Agents [50.87647372904382]
  We introduce Mem-W, a series of latent-memory-native GUI agents that treat memory as part of the agent's continuous context.<n>Across four web and mobile navigation benchmarks, Mem-W consistently improves diverse backbones and memory-enhanced baselines.
  arXiv  Detail & Related papers  (2026-05-10T04:31:23Z)
• ClawMark: A Living-World Benchmark for Multi-Turn, Multi-Day, Multimodal Coworker Agents [77.22389710754452]
  We introduce bench, a benchmark for coworker agents built around multi-turn multi-day tasks.<n>The current release contains 100 tasks across 13 professional scenarios, executed against five stateful sandboxed services.<n>The strongest model reaches 75.8 weighted score, but the best strict Task Success is only 20.0%, indicating that partial progress is common.
  arXiv  Detail & Related papers  (2026-04-26T16:05:02Z)
• Springdrift: An Auditable Persistent Runtime for LLM Agents with Case-Based Memory, Normative Safety, and Ambient Self-Perception [0.20305676256390928]
  We present Springdrift, a persistent runtime for long-lived LLM agents.<n>We introduce the term Artificial Retainer for this category.<n>This is a technical report on a systems design and deployment case study, not a benchmark-driven evaluation.
  arXiv  Detail & Related papers  (2026-04-06T13:14:37Z)
• MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks [55.145729491377374]
  Existing evaluations of agents with memory typically assess memorization and action in isolation.<n>We introduce MemoryArena, a unified evaluation gym for benchmarking agent memory in multi-session Memory-Agent-Environment loops.<n> MemoryArena supports evaluation across web navigation, preference-constrained planning, progressive information search, and sequential formal reasoning.
  arXiv  Detail & Related papers  (2026-02-18T09:49:14Z)
• Learning to Share: Selective Memory for Efficient Parallel Agentic Systems [49.78267008828593]
  Agentic systems solve complex tasks by coordinating multiple agents that iteratively reason, invoke tools, and exchange intermediate results.<n>Recent approaches deploy multiple agent teams running in parallel to explore diverse reasoning trajectories.<n>We propose Learning to Share (LTS), a learned shared-memory mechanism for parallel agentic frameworks.
  arXiv  Detail & Related papers  (2026-02-05T18:20:21Z)
• FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents [53.03492387564392]
  We introduce FS-Researcher, a file-system-based framework that scales deep research beyond the context window via a persistent workspace.<n>A Context Builder agent browses the internet, writes structured notes, and archives raw sources into a hierarchical knowledge base that can grow far beyond context length.<n>A Report Writer agent then composes the final report section by section, treating the knowledge base as the source of facts.
  arXiv  Detail & Related papers  (2026-02-02T03:00:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.