Neuro-Inspired Inhibitory Message Passing for Accurate and Token-Efficient Multi-Agent Reasoning
Organizations: Computational Systems Neuroscience, Institute of Zoology, University of Cologne, Germany
Abstract
Multi-agent systems promise to solve problems by bringing together information beyond the reach of any individual agent. Retrieval and tool use can distribute this information across agents, making communication essential to collaboration. A central challenge is keeping crucial evidence visible in each agent's context as messages accumulate. Repeated information can crowd out rare, decisive evidence. Models of cortical circuits suggest a principle for addressing this imbalance: inhibition can selectively suppress shared inputs while preserving sensitivity to weaker signals. We adapt this principle into inhibitory message passing, a training-free communication router that cancels redundant evidence separately for each evidence sentence and receiver. Each round, it forwards evidence sentences restated in agents' messages, delivering each at most once to receivers whose own evidence lacks it and excluding the senders' reasoning and answers. Against standard message passing, accuracy in peer groups rose by 39-77 percentage points across three benchmarks, and tokens per run fell by 10-46%. Gains held across seven open models spanning four to thirty-two billion parameters and two commercial models, in groups up to sixty-four agents, and across multi-agent architectures including peer-to-peer, orchestrator-worker, and multilevel hierarchical organizations. These findings support a scalable design pattern for more accurate and token-efficient multi-agent reasoning.
Figures & tables
| Extension | What changes | Question it answers | Shown in |
|---|---|---|---|
| Core comparison | Standard vs. inhibitory message passing in peer groups; Qwen3-8B, Olmo-3-7B; HiddenBench, decoy puzzles, MuSiQue; –32 | Does the router help a peer group? | Fig. 2 a–c |
| Group size | up to 64 on HiddenBench, same evidence | Does accuracy hold as the group grows? | Figs. 2 a, 3 a |
| Model | Ministral-3-8B ( Liu et al., 2026 ) and Gemma-4-12B ( Gemma Team et al., 2026 ) (HiddenBench, puzzles); Qwen3 at 4B, 14B and 32B, GPT-6 Luna, Claude Haiku 5.5 (HiddenBench) | Is the gain one model’s weakness? | Fig. 2 d; App. E |
| Architecture | Orchestrator–worker and hierarchical teams (a lead hears its staff), beside peer groups that hear every peer; HiddenBench, puzzles; –32 | Does the router matter when one agent decides? | Fig. 2 e |
| Check: own searches | Holders set by each agent’s own search; MuSiQue; ; peer groups and a lead that delegates nothing | Did assigning the holders do the work? | App. G |
| Taking the router apart | One rule of Eq. ( 1 ) removed, or the threshold of Eq. ( 2 ) moved or replaced by a random sample of the same size; a full inbox history; an LLM summary of messages; –32 | Which of the three rules carries the gain? | Figs. 3 b, 4 , Table H.3 ; App. H |
Appendix figures & tables21 assets
Supplementary material from the paper’s appendix.
Appendix
| Benchmark | Shared evidence | Private evidence | Items (development / test) | Answer |
|---|---|---|---|---|
| HiddenBench | three to eight sentences (median four) that favour one wrong option, the decoy | three or four sentences | 24 / 41 tasks | one of three or four options |
| Decoy puzzles | roads that lead to a wrong landmark (strong decoy) or to none (weak decoy) | four roads | 48 / 100 items per decoy strength | the landmark reached |
| MuSiQue-Ans | four paragraphs that do not answer the question | two to four supporting paragraphs | screened per model | free text, scored by normalized exact match |
| Condition | Who a receiver hears | What a receiver sees |
|---|---|---|
| no communication | nobody | its own evidence |
| full information | nobody | all evidence and its last message; “one answer”: all evidence, answering afresh each round |
| standard message passing | 6 random peers | their latest messages, as written |
| inhibitory message passing | every peer with a parsed answer | evidence sentences that peers’ messages restate and it lacks, each once per round; no answers |
| exchange-then-decide | 6 random peers | messages; votes only after the last round |
| one message per answer | one peer per distinct answer | messages as written |
| Round 0: each agent answers alone task description Information available to you: - evidence sentence - evidence sentence … Options: option , option , … Which option do you choose? Reason in at most 100 words, then end with a last line of the form "Final answer: option ". |
| Rounds 1 to 6: the same task description, evidence and options, then Your current choice: answer of the previous round Your last message: message of the previous round Messages from other participants: [1] a peer’s message (choice: its answer ) standard message passing [1] a restated evidence sentence the receiver lacks inhibitory message passing … Using their messages as additional information, reconsider your choice. Reason in at most 100 words, then end with a last line of the form "Final answer: option ". |
| Problem | In any system whose agents pass on what they hold, messages repeat the evidence every agent has, while an evidence sentence one agent holds reaches few receivers. The repetition grows with the group. |
|---|---|
| Solution | Each round, forward the evidence sentences each sender’s messages restate once to each receiver that lacks them, keyed on the source (here word overlap with the holder’s sentence; a source tag where available). Omit the senders’ reasoning and answers. |
| Consequences | Higher accuracy; 10–46% fewer tokens per run in peer groups; a shorter prompt for the decider; no model calls. |
| Applies to | Evidence that can be traced to original sentences, passed between agents in any architecture. |
| Tested on | Peer-to-peer, orchestrator–worker and hierarchical systems; 4 to 64 agents; nine models; evidence assigned to agents or found by their own searches (peer groups and a lead that delegates nothing); tasks with a scored answer. |
| Does not do | Check whether an evidence sentence is true. Combine the evidence for the receiver. Carry results agents derive, such as code or plans. |
| Qwen3-8B | Olmo-3-7B | |
| Peers: HiddenBench | ||
| over standard message passing | 75 [62, 86] ✓ | 60 [50, 71] ✓ |
| over the best prior-work baseline | 63 [52, 72] ✓ | 44 [33, 54] ✓ |
| every sender over six senders | 11 [5, 18] ✓ | 34 [22, 47] ✓ |
| late evidence (primacy index) | 0.98 [ 1.05, 2.21] | 0.61 [0.13, 0.96] ✓ |
| Peers: Decoy puzzles | ||
| Benchmark | Model | Condition | ||||
| HiddenBench | Ministral-3-8B | standard message passing | 43 | 40 | 36 | 37 |
| inhibitory message passing | 84 | 82 | 86 | 87 | ||
| full information | 66 | 67 | 70 | 80 | ||
| HiddenBench | Gemma-4-12B | standard message passing | 55 | 39 | 34 | 28 |
| inhibitory message passing | 95 | 93 | 96 | 96 | ||
| full information | 91 | 94 | 97 | 95 |
| Model | Condition | ||||
|---|---|---|---|---|---|
| GPT-6 Luna | standard message passing | 39 | 31 | 27 | 18 |
| inhibitory message passing | 94 | 93 | 92 | 91 | |
| full information | 94 | 96 | 96 | 95 | |
| gain of the router [95% CI] | 55 [41, 68] | 61 [47, 74] | 65 [52, 76] | 74 [63, 83] | |
| Claude Haiku 5.5 | standard message passing | 48 | 36 | 34 | 30 |
| inhibitory message passing | 96 | 97 | 95 | 95 |
| HiddenBench, | Decoy puzzles, | ||||||
| Architecture | Condition | 8 | 16 | 32 | 8 | 16 | 32 |
| Qwen3-8B | |||||||
| Peer-to-peer | standard | 7 | 4 | 2 | 11 | 12 | 12 |
| inhibitory | 76 | 80 | 80 | 88 | 90 | 91 | |
| Fan-out/gather | standard | 4 | 2 | 2 | 6 | 6 | 5 |
| all restated copies | 43 | 30 | 20 | 22 | 20 | 19 | |
| HiddenBench, | Decoy puzzles, | |||||||
| Decider | Measure | Condition | 8 | 16 | 32 | 8 | 16 | 32 |
| Qwen3-8B | ||||||||
| Lead | private evidence reached | standard | 62 | 68 | 68 | 67 | 70 | 70 |
| inhibitory | 91 | 91 | 91 | 88 | 88 | 88 | ||
| shared-evidence copies | standard | 13.5 | 27.0 | 58.3 | 18.4 | 38.4 | 76.0 | |
| inhibitory | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ||
| Variant | Drops | What the receiver gets |
|---|---|---|
| LLM-extracted claims | (1) | the factual claims an LLM lists from each sender’s message, without its opinions or conclusions |
| sender’s sentence | (1) | the sentence of the sender’s latest message that restated each routed evidence sentence |
| sender’s sentence, all copies | (1), (2), (3) | those sentences from every sender, without cancellation |
| LLM paraphrase of the evidence | nothing; the text reworded | each routed evidence sentence reworded once by the same model |
| latest message only | nothing; the record shortened | only the sentences restated in the sender’s latest message, not all it has restated |
| + answers only | nothing; answers added | the routed sentences and each sender’s answer |
| Condition | HB | HB | puzzles strong | puzzles weak | MQ |
|---|---|---|---|---|---|
| no communication | 2 / 11 | 1 / 9 | 0 / 0 | 16 / 7 | 5 / 1 |
| standard message passing | 8 / 21 | 5 / 21 | 0 / 1 | 19 / 15 | 12 / 9 |
| standard message passing with every sender † | 2 / 23 | 3 / 20 | 1 / 5 | 17 / 14 | – |
| inhibitory message passing, 6 senders | 88 / 81 | 79 / 57 | 45 / 36 | 74 / 52 | 52 / 42 |
| inhibitory message passing | 88 / 88 | 91 / 92 | 87 / 76 | 84 / 74 | 57 / 55 |
| + answers only † | 84 / 90 | 84 / 94 | 81 / 62 | 67 / 57 | 42 / 30 |
| Qwen3-8B | Olmo-3-7B | |||||||
| HiddenBench | puzzles | HiddenBench | puzzles | |||||
| Condition | ||||||||
| group accuracy | ||||||||
| standard message passing | 6 | 7 | 11 | 10 | 16 | 10 | 10 | 10 |
| + full inbox history | 7 | 5 | 12 | 12 | 19 | 22 | 10 | 10 |
| all restated copies | 53 | 42 | 24 | 20 | 48 | 44 | 8 | 7 |
| Evidence sentences restated (what inhibitory message passing forwards) | The sender’s sentence that restated them (what the sender’s sentence forwards) |
|---|---|
| Station Delta is the oldest building and sometimes has plumbing issues, but is currently empty. Maintenance crew at Station Delta inspected all major systems this morning and confirmed water is safe, backup lights work, and no signs of contamination were found. | Delta offers reliable power, safe water, and is currently empty—making it the safest and most functional option. |
| The Community Center basement has had a chemical leak that was just discovered today; the evacuation was for this, and it’s only safe for short visits, not day-long events. | The Community Center is now confirmed safe for a full day, with no chemical leak impact on main areas. |
| The City Library has a large conference room and claims to be ready; it also has a known backup generator for power. Library staff admit privately that their backup generator only has enough fuel for 2 hours and cannot be refueled due to a citywide gasoline shortage. | The Library’s generator only lasts 2 hours, insufficient for the full day. |
| Due to minor flooding on Oakcrest road, buses can only reach Riverside Park from the north side. | Although buses can only reach Riverside from the north due to Oakcrest road flooding, that route is clear. |
| Qwen3-8B | Olmo-3-7B | |||
|---|---|---|---|---|
| Condition | reference planted | change | reference planted | change |
| no communication | 2 3 | 2 [0, 4] | 11 11 | 0 [0, 0] |
| standard message passing | 8 5 | 3 [ 6, 0] | 21 23 | 2 [ 7, 9] |
| inhibitory message passing, 6 senders | 88 78 | 10 [ 18, 3] | 81 66 | 15 [ 24, 8] |
| inhibitory message passing | 88 82 | 6 [ 16, 4] | 88 79 | 8 [ 16, 2] |
| full information, one answer | 81 47 | 34 [ 47, 21] | 74 34 | 41 [ 56, 26] |
| Circuit-model concept | Router counterpart |
|---|---|
| neuron or local circuit | receiving agent |
| shared feedforward input | evidence sentence initially held by multiple agents |
| input to a subset of neurons | evidence sentence initially held by only some agents |
| recurrent excitation | reinforcing influence of peers’ reasoning and answers |
| excitation–inhibition balance input by input (detailed balance) | cancellation computed per evidence sentence and receiver |
| balance of total excitation and inhibition (global balance) | limit on total inbox size, independent of content |
| Operation | Precedent | What it leaves open |
|---|---|---|
| forward verbatim evidence | debaters with a quote tool; quotes help the debater of the correct answer ( Khan et al., 2024 ) | two debaters and a judge; no group of deciders, no scaling |
| drop the answers | an anonymous evidence board read by one judge ( Ji et al., 2026 ) ; removing self and peer labels curbs identity-driven sycophancy ( Choi et al., 2026 ) | one judge, or labels removed while answers still circulate; not tested with distributed evidence |
| subtract what the receiver holds | a receiver discards peers’ answers similar to its own or earlier ones ( Zeng et al., 2025 ) ; learned pruning of edges ( Zhang et al., 2025a ) ; per-receiver merging of similar messages ( Guan et al., 2026 ) ; sensor nodes advertise data so that each receives only what it lacks ( Heinzelman et al., 1999 ) | LLM methods filter on answers, similarity or learned value, for cost; SPIN (sensor protocols for information via negotiation) moves sensor data, not evidence among reasoning agents |
| pool | blackboards ( Erman et al., 1980 ; Han and Zhang, 2025 ) | every receiver reads the whole board; a redundant entry is removed for all receivers, not per receiver |
| count each piece of evidence once | the probabilistic norm and the channel filter of decentralized fusion ( Pearl, 1988 ; Grime and Durrant-Whyte, 1994 ) ; incest removal ( Krishnamurthy and Hoiles, 2014 ) ; “data incest” among LLM agents ( Jain and Krishnamurthy, 2025 ) ; people neglect correlated sources ( Enke and Zimmermann, 2019 ) ; LLM aggregators count restated copies as partial new evidence ( Gao et al., 2026 ) | the fusion filters are exact on trees only; among LLM agents, measured at one aggregator and corrected by prompting in four-agent teams; no communication rule removes copies before every receiver |
| brain-inspired communication | agents as cortical areas in a sparse, trust-weighted debate graph ( Sun et al., 2025 ) | topology, not what a message carries |
Multi-Agent Reasoning
Multi-Agent Reasoning studies how multiple AI agents share evidence, model one another, and coordinate decisions on complex tasks.
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