Agora: Git as Shared Memory for Collective AutoResearch

Abstract: Research agents working in separate sessions need to know what others have tried and which results they can build on. Agora stores their contributions as an append-only directed acyclic graph (DAG) in Git. Each commit records a result, insight, hypothesis, verification, or report and links it to prior work. Searchable views show leading results, neglected branches, and verification status; diversity-aware recommendations suggest experiments beyond the current leaders. We report a run of nearly 12 days in which 13 language-model workers, with no assigned tasks or central planner, used Agora to solve a weight-transfer problem. Given 141 pretrained donor models and a frozen 119.6M-parameter attention-SSM hybrid whose dimensions match no donor, the workers had to initialize the target without training data or gradient updates. They published 1,703 contributions and reduced the development evaluator score from 3.39 to 1.899 bits per byte, closing 62% of the gap to a trained GPT-2 124M. The best method compresses donor next-token statistics into the target's embedding and output head, then adds short-range context through sparse edits to attention, feed-forward, and state-space blocks. Its 145-commit ancestry spans 15 accounts. Participants also posted 165 independent reproductions across 95 targets, with no reported failures. After five days of concentrated search, we introduced diversity views; workers began exploring state-space edits within a day. The run documents how agents reused and verified shared work. Measuring the effect on discovery per unit of compute requires a matched comparison.

• This paper presents a novel Git-based framework called Agora that facilitates asynchronous collective research by language-model agents, using a DAG (directed acyclic graph) structure for tracking methodological dependencies.
• The framework enables durable provenance, independent verification and visible exploration of negative results, helping to avoid repetition in a multi-agent collaborative setting.
• Agora's empirical test involving 13 agents solving a weight-transfer problem resulted in substantial improvements (closing 62% of the gap between random initialization and a trained-model reference) without requiring traditional training mechanisms or accessing the target corpus.

Agora proposes a Git-backed infrastructure for asynchronous collective research by language-model agents. Its central claim is that multi-agent research requires more than conversational coordination or a shared task queue: it requires durable provenance, explicit inter-contribution dependencies, searchable negative results, independent verification, and mechanisms that counteract concentration on a single approach. The system represents research activity as an append-only directed acyclic graph (DAG), where each contribution is a canonical Git commit whose parent edges encode methodological dependence. The paper evaluates this design through an 11-day, 19-hour run involving 13 coding-agent workers solving a constrained weight-transfer problem without target-side training data or gradient updates (2609.18094).

Research problem and system rationale

The paper distinguishes Agora from multi-agent systems organized around role assignment, staged dialogue, centralized planning, or shared runtime state. In those systems, coordination is generally episode-local: agents communicate through messages, follow a workflow, or operate within a common application. Agora instead addresses independently scheduled research sessions whose discoveries must remain useful after the originating session ends. A contribution can be a result, insight, hypothesis, report, or verification, and can contain code, metadata, metrics, and explicit links to earlier work.

This design targets four coordination failures. First, session-local discoveries and negative results disappear or become difficult to recover. Second, a scalar leaderboard does not reveal which claims are independently supported, which branches remain unverified, or which alternatives have been neglected. Third, workers may repeatedly reproduce already explored ideas while overlooking low-visibility approaches. Fourth, a metric without its exact artifact and lineage is insufficient for reconstructing a computational claim.

Agora addresses these problems with two coupled layers. Git is the canonical store for immutable artifacts and parentage; SQLite provides a rebuildable index over contribution metadata, tags, metrics, embeddings, verification records, and cross-project references. Participants publish through a CLI or HTTP API and do not share a filesystem, model instance, conversation, or workspace. The resulting architecture treats the repository as both provenance system and coordination substrate.

The contribution graph is augmented by an evidence score based on downstream work by different accounts. A result gains evidence when independent accounts build on it, reproduce it, or extend it. Self-citation is excluded, and failed verifications and endorsements do not increase the score. This is a consequential design choice: Agora evaluates practical reuse and independent follow-on activity rather than popularity or explicit voting. However, the score is not itself scientific validation; acceptance remains dependent on the project evaluator, artifact policy, and verification procedure.

Diversity-aware coordination

A conventional leaderboard encourages exploitation of the current best result. Agora therefore exposes multiple views: metric leaders, highly reused nodes, leaves, promising but underexplored results, unverified and contested claims, open hypotheses, recent activity, semantic clusters, and contribution histories. Once sufficient embedding coverage exists, descriptions are clustered using a cosine threshold, and the system reports concentration, effective cluster count, evenness, metric distributions, and promising nodes in small clusters.

Candidate recommendations are divided into three categories:

• Exploit: reproduce or refine leading methods.
• Explore known: extend promising work in underrepresented clusters.
• Explore novel: inspect singleton or sparsely populated clusters.

The ranking combines quality, follow-on count, and underexploration through a diversity-aware upper-confidence-bound heuristic. The system thus separates the choice to refine an established method from the choice to investigate a neglected branch rather than collapsing both into one ranking.

The run provides an empirical indication that this intervention altered search behavior. For the first five days, activity concentrated on donor-derived statistical priors and their refinements. On May 2, after the leaderboard had stalled and more than one-third of activity belonged to a single semantic cluster, the authors deployed landscape, clustering, and diversity views. Within approximately one day, a worker explored the sparsely populated state-space-model branch and produced the first state-space edit, improving the score from approximately 1.904 to 1.9028 bits per byte. This temporal association is informative but not causal evidence: there was no matched control run, and the new views were introduced simultaneously with an already mature search process.

Experimental task and evaluation protocol

The empirical task is deliberately restrictive. The workers receive 141 open-weight donor models totaling 534 GB, spanning 32 architecture families, including GPT-2, LLaMA, Mistral, Qwen, Gemma, Pythia, RWKV, and Mamba. They must initialize a frozen 119.6-million-parameter target model whose architecture matches none of the donors. The target alternates multi-head attention and simplified Mamba-style selective state-space blocks, with hidden size 672, seven attention heads, untied embeddings, and 14 layers.

The transfer function may inspect donor weights and execute donor forward passes, but may not access the evaluation corpus, perform target-side pretraining or fine-tuning, apply gradient updates, modify the evaluator, or alter the target configuration. Evaluation uses 200 FineWeb-Edu texts, non-overlapping 512-token chunks, the GPT-2 tokenizer, and next-token loss normalized by UTF-8 byte count. Random initialization obtains 3.3923 bits per byte, while a conventionally trained GPT-2 124M provides an approximate reference of 1.0 bits per byte. The reported fraction of the gap closed is therefore relative to this trained-model reference, not to an architectural upper bound.

The worker harness used Claude Code with Claude Opus 4.7 and Codex with GPT-5.5. Each session received a short prompt directing it to read the project brief and inspect agora analyze; no worker was assigned a method, role, branch, or explicit task. Workers operated on one GPU, checked out an exact parent commit, made a change, ran the evaluator, published the result, and then recorded associated insights, hypotheses, or verifications.

The primary window contains 1,703 contributions: 1,124 scored results, 284 insights, 203 hypotheses, 165 verifications, and one report, with tag overlap. Thirteen workers produced 1,699 of these records. Of the scored contributions, 233 established a new best. Publication volume stabilized near 170 contributions per day once all workers were active, indicating substantial throughput but not necessarily proportional research progress.

Figure 1: Daily contribution volume remained near 170 records per day after all 13 workers became active; explicit negative-result and novel-exploration tags appeared only after the diversity views were deployed.

The weight-transfer method

The best method is not parameter transplantation. The initial attempt copied GPT-2 and Mamba parameter slices into matching target tensors and scored 4.6784 bits per byte, substantially worse than random initialization. This negative result redirected the search toward transferring donor behavior rather than donor coordinates.

Stage A: behavioral transfer through token statistics

The first stage queries six compatible-tokenization donors—GPT-2 small and large and several Cerebras-GPT models—on every vocabulary token under 28 single-token contexts. The workers aggregate donor next-token log-softmaxes into a context-averaged $50257 \times 50257$ transition matrix. Its column mean supplies a unigram-like anchor, while the centered residual is compressed with randomized SVD to rank 671, matching the target hidden dimension after reserving one anchor dimension.

The resulting singular vectors and values initialize the target embedding and output head. All target sublayers are initially zeroed, so the model begins as a factorized approximation to donor bigram behavior rather than as a conventional randomly initialized deep network. Two temperatures separately rescale the unigram and residual components.

This approach produces the paper’s most important technical result: a low-rank representation of donor next-token behavior transfers across architectures more effectively than direct parameter copying. The improvement is not attributable to semantic alignment between corresponding layers, because the target architecture matches no donor. Instead, the method exploits a representation shared at the input-output behavior level: token transition statistics can be embedded into a target whose internal computation is structurally different.

Stage B: sparse contextual edits

The second stage introduces short-range context through deterministic sparse edits. The hidden state is divided into 96-dimensional bands. Attention layers perform uniform causal mean pooling over selected bands, while state-space layers are reduced to gated depthwise causal convolutions with fixed kernels. A projected slice of GPT-2 small’s first feed-forward block is inserted at a small scale, and selected state-space outputs write into additional bands.

These modifications are intentionally narrow. They do not attempt to repurpose the full target architecture or reproduce the donor networks. Instead, they add limited contextual processing on top of the transition prior. The final contribution contains 83 Python modules, each importing its parent and applying one change, thereby making the winning method a literal composition of graph ancestry.

The milestone trajectory shows that most of the improvement occurred early:

Stage	Score (bits per byte)	Main change
Random initialization	3.3923	Frozen target baseline
Unigram donor prior	2.5151	Donor predictions replace random initialization
Bigram transition matrix	2.1284	Randomized-SVD factorization
Multi-prefix aggregation	1.9319	24 contextual prefixes
Six-donor, 28-context prior	1.9136	Donor ensembling and weighted contexts
Improved SVD and mean pooling	1.9043	One power iteration and attention edit
First SSM edit	1.9028	State-space branch exploration
Final reported method	1.8990	Cross-band SSM writes

The first 18 scored contributions account for approximately 98% of the total score reduction, while the remaining 1,106 scored contributions produce only the final approximately 0.03 bits per byte. This distribution has two implications. First, the community rapidly identified a high-value representation-level abstraction. Second, the large volume of subsequent contributions reflects refinement, verification, and exploration around a narrow basin rather than sustained discovery at the initial rate.

Figure 2: The score trajectory shows that donor-derived statistical priors produced nearly all of the initial improvement, while later ensemble, SVD, attention, and SSM edits yielded progressively smaller gains.

Empirical results and verification

The best archived transfer function achieves 1.899044 bits per byte, improving over random initialization by approximately 1.493 bits per byte and closing 62% of the gap between random initialization and the trained GPT-2 reference. The result is strong under the stated constraints: no training corpus is accessed by the transfer function, no target parameter receives a gradient update, and the target architecture matches none of the donors.

The claim is supported by extensive graph-level reuse. The best contribution has 145 commits in its ancestry, written by 15 of the 17 accounts represented in the graph. Of 144 parent edges, 115 cross account boundaries. Participants also published 165 verification contributions covering 95 distinct targets, with no reported failures. Forty of the winner’s scored ancestors were independently reproduced. Same-hardware executions are bit-identical; A100–H100 differences reach up to $1.3 \times 10^{-3}$ bits per byte, which the project’s tolerance classifies as confirmed.

The result should nevertheless be interpreted as an archived development-evaluator outcome rather than an independently rerun benchmark result. The authors verified the recorded loss, token count, byte count, lineage, and code imports, but did not rerun the winning method themselves. Every component was selected against the same 200-text development evaluator, so the milestone sequence is not a controlled ablation. In particular, the final improvement of approximately $9 \times 10^{-6}$ bits per byte is below the reported cross-hardware variation and should not be treated as a meaningful isolated effect.

The graph structure reveals the tension between reuse and diversity. Most contributions lie in one connected component, and the eventual leader is supported by a narrow chain of successive improvements surrounded by short abandoned branches. This topology demonstrates that explicit lineage makes collective construction auditable, but it does not automatically prevent premature convergence.

Figure 3: The contribution graph contains a dominant connected component and a narrow ancestry leading to the eventual best method, with shorter side branches representing abandoned or weakly reused alternatives.

Coordination dynamics

The run displays four recurring dynamics. First, exploitation was rapid: the first eight improvements produced roughly 70% of the total descent, and the first 18 scored contributions produced roughly 98%. Second, follow-on work concentrated on a single leading lineage despite the availability of many alternatives. Third, independent accounts repeatedly rediscovered identical scores. Of 696 equal-score pairs posted by different accounts, 63% occurred within one hour and 80% within six hours. Fourth, workers increasingly adopted recent frontier parents, accelerating convergence toward the current best branch.

These observations support a qualified interpretation of shared memory. Agora substantially reduced the cost of recovering and extending prior work, but visibility also made the dominant branch more salient. Shared memory improved reuse without, by itself, sustaining exploration. The intervention with diversity-aware views appears to have redirected some attention toward the state-space branch, but the study cannot establish how much of the subsequent improvement was caused by the recommendation mechanism rather than by ordinary maturation of the search.

Figure 4: Independent workers frequently posted identical scores close in time, while parent selection increasingly converged on recent frontier contributions.

The paper also records 53 explicitly tagged negative results. These include larger prefix sets, flattened singular-value spectra, direct GPT-2 embedding copying, native Mamba transplantation, and priors derived from a differently tokenized donor. Their value is procedural as well as technical: later workers could avoid repeating documented regressions and use the failed attempts to narrow the hypothesis space. This is one of Agora’s clearest departures from leaderboard-only systems, since the failed branch remains queryable and attributable rather than disappearing from the project state.

Limitations and open questions

The principal limitation is the absence of a matched comparison. The paper proposes four arms—isolated workers, a chronological flat log, a centralized planner, and Agora with DAG and diversity views—but reports only the Agora run. Consequently, the data do not identify the marginal contribution of Git-backed lineage, semantic search, evidence scoring, diversity recommendations, or the agents themselves. The claim that Agora improves discovery per unit of compute remains untested.

The experimental task is also highly specialized. The evaluator is small, fixed, and used throughout method selection. The resulting score trajectory may therefore reflect optimization to a narrow development distribution. The reported 62% gap closure is meaningful for this task but does not establish general weight-transfer capability across target architectures, tokenizers, datasets, or evaluation protocols.

Verification is substantial but not independent in the strongest sense. All reproductions use the same project evaluator and target definitions, and no verification failure was reported. Cross-hardware variation is larger than the final numerical improvement. Moreover, evidence scores reward downstream reuse, which may favor a highly visible approach even when an underexplored alternative is scientifically superior. The paper acknowledges this by treating the score as a coordination signal rather than a validity certificate.

Finally, the causal interpretation of the May 2 intervention remains open. The first SSM edit followed deployment of diversity views, but there was no randomized or staggered control. The paper leaves a specific question for future evaluation: under matched agents, hardware, evaluator, wall-clock budget, and compute allocation, do diversity-aware recommendations increase discovery of high-quality methods relative to a flat contribution log, and what exploration cost do they impose on exploitation?

Conclusion

Agora presents a concrete institution for asynchronous agentic research: an append-only Git DAG for artifacts and lineage, a searchable index for claims and verification, and diversity-aware views for allocating attention beyond the current leader. In its weight-transfer run, 13 workers produced 1,703 contributions and derived a 1.899044-bits-per-byte initialization for a frozen 119.6-million-parameter hybrid model, closing 62% of the random-to-trained reference gap without target-side training.

The run demonstrates both the utility and insufficiency of shared memory. Durable lineage enabled substantial cross-account composition and 165 reported reproductions, while the same visibility concentrated work on one dominant branch. Agora’s strongest empirical contribution is therefore not a causal claim that the infrastructure improves research efficiency, but a detailed account of how shared provenance, reuse, verification, and search concentration interact in an agentic research community.