Human Feedback Types
provisional (inferred)None explicit
No explicit feedback protocol extracted.
"Multi-agent LLM systems coordinate through natural-language messages that consume 40--60\% of their token budget."
HFEPX · Eval paper review
Routed Graph Handoff: Adaptive Format Selection for Multi-Agent LLM Delegation
Pratyay Banerjee, Ankit Chadha
Published
Aug 26, 2026
Citations
0
Trust level
Provisional
Usefulness score
Unavailable
Extraction confidence
0% (Provisional)
Derived from abstract and metadata only.
Signals refreshed
Aug 26, 2026
Should you rely on this paper?
Signal extraction is still processing. This page currently shows metadata-first guidance until structured protocol fields are ready.
This page is a lightweight research summary built from the abstract and metadata while deeper extraction catches up.
Abstract-only analysis, low confidence
All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.
- This page is still relying on abstract and metadata signals, not a fuller protocol read.
Best use
Background context only
Use if you need
A provisional background reference while structured extraction finishes.
What to verify
Read the full paper before copying any benchmark, metric, or protocol choices.
Main weakness
This page is still relying on abstract and metadata signals, not a fuller protocol read.
Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
Unavailable
Provisional (processing)
Eval-fit score is unavailable until extraction completes.
If you are doing eval pipeline work, start here
Abstract
Multi-agent LLM systems coordinate through natural-language messages that consume 40--60\% of their token budget. Replacing these with structured graphs reduces cost but fails on tasks requiring adaptive reasoning. We propose \textbf{Routed Graph Handoff}, where a lightweight LLM router (155 tokens, 0.15\% overhead) selects between a typed dependency graph and natural language for each delegation. On four benchmarks (1,050+ trajectories), the routed system matches or exceeds NL-only on every task: \textbf{+12.7\,pp} on $τ$-retail at 3.2$\times$ compression ($p{<}0.01$), \textbf{+8.7\,pp} on BrowseComp at 2.2$\times$ compression ($p{<}0.05$), and parity on BFCL and AppWorld. Without the router, graph-only delegation regresses 14.6\,pp on AppWorld; the router eliminates this at near-zero cost. A graph-aware executor prompt is required: the same schema without interpretation guidance yields no gain. An oracle analysis reveals 8.6\,pp of additional headroom, motivating execution-time adaptive routing as future work.
What we could verify
These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.
Evaluation Modes
provisional (inferred)None explicit
Validate eval design from full paper text.
"Multi-agent LLM systems coordinate through natural-language messages that consume 40--60\% of their token budget."
Quality Controls
provisional (inferred)Not reported
No explicit QC controls found.
"Multi-agent LLM systems coordinate through natural-language messages that consume 40--60\% of their token budget."
Benchmarks / Datasets
provisional (inferred)Not extracted
No benchmark anchors detected.
"Multi-agent LLM systems coordinate through natural-language messages that consume 40--60\% of their token budget."
Reported Metrics
provisional (inferred)Not extracted
No metric anchors detected.
"Multi-agent LLM systems coordinate through natural-language messages that consume 40--60\% of their token budget."
Rater Population
provisional (inferred)Unknown
Rater source not explicitly reported.
"Multi-agent LLM systems coordinate through natural-language messages that consume 40--60\% of their token budget."
Human feedback details
This page is using abstract-level cues only right now. Treat the signals below as provisional.
- Potential human-data signal: No explicit human-data keywords detected.
- Potential benchmark anchors: No benchmark names detected in abstract.
- Abstract highlights: 3 key sentence(s) extracted below.
Evaluation details
Evaluation fields are inferred from the abstract only.
- Potential evaluation modes: No explicit eval keywords detected.
- Potential metric signals: No metric keywords detected.
- Confidence: Provisional (metadata-only fallback).
Research brief
Metadata summary
Multi-agent LLM systems coordinate through natural-language messages that consume 40--60\% of their token budget.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Key takeaways
- Multi-agent LLM systems coordinate through natural-language messages that consume 40--60\% of their token budget.
- Replacing these with structured graphs reduces cost but fails on tasks requiring adaptive reasoning.
- We propose \textbf{Routed Graph Handoff}, where a lightweight LLM router (155 tokens, 0.15\% overhead) selects between a typed dependency graph and natural language for each delegation.
Researcher actions
- Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
- Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
- Use related-paper links to find stronger protocol-specific references.
Caveats
- Generated from abstract + metadata only; no PDF parsing.
- Signals below are heuristic and may miss details reported outside the abstract.