IdeaTrail: Full-Process Agent Trajectories for Scientific Ideation

# IdeaTrail: Full-Process Agent Trajectories for Scientific Ideation
Source: [https://arxiv.org/html/2607.10144](https://arxiv.org/html/2607.10144)
###### Abstract

Scientific research is a complex, multi\-stage workflow rather than a single act of text generation\. The ideation process typically emerges through literature search, paper reading, tool use, claim checking, cross\-paper synthesis, brainstorming, rejection of weak directions, and iterative writing\. Existing resources capture individual components of this process, but datasets that jointly record tool use, evidence acquisition, intermediate artifact evolution, and idea\- or proposal\-level endpoints remain limited\. This report introducesIdeaTrail, a multi\-turn process\-trajectory dataset for scientific ideation and proposal generation\. Each instance records a research process from evidence gathering to either idea selection or proposal construction\. Rather than freely fabricating trajectories,IdeaTrailstarts from human\-selected high\-quality research papers and proposal artifacts and uses a Generator–Advisor synthesis loop\. The Generator produces the visible trajectory through actions, observations, and artifact edits, while the Advisor has access to the full generation context and checks grounding, causal order, naturalness, and leakage from hidden targets\. This reverse\-to\-forward procedure produces multi\-turn research data that remains aligned with real scientific artifacts while approximating the uncertainty, evidence use, and staged convergence of research practice\.IdeaTrailprovides both a dataset and a general recipe for synthesizing process\-supervision data for scientific research agents\.

## 1Introduction

Scientific research is a long\-horizon agentic workflow, not a single step of text generation\. Research ideas and proposals emerge through multiple rounds of literature search, tool use, paper reading, evidence checking, cross\-paper synthesis, brainstorming, idea selection, and proposal writing\. Recent AI scientist systems increasingly automate substantial portions of this workflow, from hypothesis generation to experimentation and manuscript production\[[3](https://arxiv.org/html/2607.10144#bib.bib3),[4](https://arxiv.org/html/2607.10144#bib.bib4),[5](https://arxiv.org/html/2607.10144#bib.bib5)\]\. In parallel, datasets and training frameworks have begun to formalize scientific ideation as idea evaluation, literature\-grounded generation, or decomposed discovery tasks\[[1](https://arxiv.org/html/2607.10144#bib.bib1),[2](https://arxiv.org/html/2607.10144#bib.bib2),[6](https://arxiv.org/html/2607.10144#bib.bib6),[7](https://arxiv.org/html/2607.10144#bib.bib7)\]\. However, public supervision for long\-horizon scientific ideation trajectories remains limited\. Existing resources typically emphasize final ideas, selected intermediate decisions, or system outputs rather than the coupled sequence of evidence discovery, tool interaction, intermediate reasoning, artifact evolution, idea selection, and proposal construction\.

This report introducesIdeaTrail, a dataset of reverse\-synthesized process supervision for scientific ideation\. EachIdeaTrailinstance records a multi\-turn research trajectory in which an agent moves from a broad research query to either an idea\-level or proposal\-level endpoint\. The trajectory is represented as a multi\-turn message stream with tool invocations, evidence\-gathering steps, reasoning turns, and artifact updates\. For proposal\-extension trajectories, the research proposal serves as the endpoint of scientific ideation and the starting point for downstream implementation\. Such a proposal should be concrete enough to support coding, experimentation, and verification by subsequent agents\. It should specify the research question, the core mechanism or method, the expected contribution, the evaluation setup, and the risks\.

Trajectories are generated through a Generator–Advisor review loop\. The Advisor has access to the full generation context, including the selected paper, the proposal endpoint, and hidden constraints\. The Generator sees only the visible query, the current trajectory prefix, the current artifacts, and a set of available tools\. It proposes actions, observations, reasoning steps, and artifact edits in the forward direction\. The Advisor evaluates whether each generated step is grounded, causally ordered, natural, and free of leakage from hidden targets\. This loop creates a controlled form of adversarial supervision: the Generator is responsible for producing a plausible research process, while the Advisor prevents the process from drifting, fabricating evidence, or revealing future information too early\.

The Generator’s tools are also cutoff\-aware\. Each case is generated under a specified information horizon, so the agent cannot rely on evidence that would be unavailable under the intended data cutoff\. This matters because scientific ideation is highly time\-sensitive\. A trajectory that uses later papers, later benchmarks, or later terminology may become easier to generate, but it no longer reflects the information state under which the target research direction should have been discovered\. Cutoff\-bounded tool use therefore reduces temporal leakage and helps align the generated process with the target scientific artifact\.

IdeaTrailalso incorporates researcher portraits as optional conditioning context\. Each portrait is distilled from the first author’s prior papers and summarizes domain knowledge, research approach, novelty style, evidence preferences, and recurring signature patterns\. The portrait serves as a research prior that makes generated trajectories less generic and more aligned with plausible researcher\-specific choices\.

In summary, we introduceIdeaTrail, a multi\-turn process\-trajectory dataset for scientific ideation and proposal generation, and propose a reverse\-to\-forward synthesis recipe for constructing process\-supervision data from existing papers\.

## 2Related Work

#### The emergence of AI scientists\.

Recent systems suggest that language\-model agents can participate in increasingly broad portions of the research lifecycle\. The AI Scientist connects ideation with implementation, experimentation, manuscript writing, and automated review\[[3](https://arxiv.org/html/2607.10144#bib.bib3)\]\. Google’s Co\-Scientist uses a multi\-agent process of generation, critique, ranking, and refinement to develop experimentally testable hypotheses\[[4](https://arxiv.org/html/2607.10144#bib.bib4)\]\. More recently, FARS demonstrated a fully automated system operating across ideation, planning, experimentation, and writing at deployment scale, while retaining proposals, code, logs, results, and manuscripts in a shared workspace\[[5](https://arxiv.org/html/2607.10144#bib.bib5)\]\. Together, these systems motivate training resources that represent research as an extended process rather than an isolated generation task\.

#### Abstractions in existing ideation supervision\.

Existing resources make scientific ideation more tractable by concentrating on selected outputs or decomposed decisions\. IdeaBench evaluates generated ideas against reference research contributions, while ResearchAgent iteratively generates and reviews literature\-grounded ideas\[[2](https://arxiv.org/html/2607.10144#bib.bib2),[1](https://arxiv.org/html/2607.10144#bib.bib1)\]\. MOOSE\-Star identifies the combinatorial difficulty of directly learningP​\(h∣b\)P\(h\\mid b\)and constructs TOMATO\-Star by decomposing paper\-derived discovery into motivation planning, inspiration retrieval, and incremental hypothesis composition\[[6](https://arxiv.org/html/2607.10144#bib.bib6)\]\. This decomposition provides scalable supervision for key discovery decisions, but intentionally abstracts away much of the temporally extended activity through which evidence is searched, read, checked, synthesized, and converted into evolving research artifacts\. Agentic\-Ideation moves toward process supervision by using oracle guidance to synthesize tool\-using ideation trajectories\[[7](https://arxiv.org/html/2607.10144#bib.bib7)\], yet reusable data capturing the broader, persistent research process remains limited\.

#### Positioning IdeaTrail\.

IdeaTrailtargets this data gap by representing scientific ideation as a long\-horizon, multi\-turn trajectory rather than only a final idea or a sequence of decomposed choices\. Each trajectory couples literature search and reading, tool observations, explicit evidence artifacts, cross\-paper synthesis, candidate comparison, and either an idea\-level or proposal\-level endpoint\. Its reverse\-to\-forward Generator–Advisor construction is designed to retain alignment with real research artifacts while exposing the intermediate process as reusable supervision\. Accordingly, the contribution ofIdeaTrailis not another autonomous scientist system, but a dataset and synthesis recipe for training and studying agents that must sustain coherent research behavior over many turns\.

## 3The Forward Process of IdeaTrail: How Research Ideation Is Generated

This section defines the forward process of ideation\. The process is a design convention rather than a claim about how every human scientist works\. Its purpose is to provide a stable training target for research agents\. Within this convention, scientific ideation is modeled as a coupled sequence of behaviors and artifacts\.

The behavior sequence describes what the agent does\. The artifact sequence records what the agent has learned and decided\. The two views are inseparable: a search action has limited training value unless it changes what the agent knows; an artifact has limited credibility unless it can be traced back to search, reading, and reasoning actions\.

### 3\.1Behavioral Trajectory: From Exploration to Proposal Writing

A trajectory is represented as a sequence of turns:

τ=\{xt\}t=1T,xt=\(bt,ut,ot,Δ​at\),\\tau=\\left\\\{x\_\{t\}\\right\\\}\_\{t=1\}^\{T\},\\qquad x\_\{t\}=\(b\_\{t\},u\_\{t\},o\_\{t\},\\Delta a\_\{t\}\),\(1\)
wherebtb\_\{t\}is the agent behavior at turntt,utu\_\{t\}is the tool call or action,oto\_\{t\}is the observation or tool result, andΔ​at\\Delta a\_\{t\}is the artifact update induced by the turn\. The artifact state evolves as

at=Update​\(at−1,Δ​at\)\.a\_\{t\}=\\textsc\{Update\}\(a\_\{t\-1\},\\Delta a\_\{t\}\)\.\(2\)
The agent first gathers evidence through search and reading, then audits claims against sources, synthesizes cross\-paper tensions and opportunities, compares candidate ideas, and finally writes an executable proposal\. The important property is staged convergence: the final idea should emerge from evidence and alternatives rather than appear as a known answer from the first turn\.

![Refer to caption](https://arxiv.org/html/2607.10144v1/Figures/scitrace_behavior_artifact_chain.png)Figure 1:Canonical forward process\. The top row shows behavioral stages\. The bottom row shows durable artifacts\. The training target is the coupled evolution of both rows\.
### 3\.2Artifact Trajectory

The artifact trajectory is the durable record of the agent’s research state:

claim\_audit\.md→synthesis\.md→idea\_candidates\.md→research\_proposal\.md\.\\texttt\{claim\\\_audit\.md\}\\rightarrow\\texttt\{synthesis\.md\}\\rightarrow\\texttt\{idea\\\_candidates\.md\}\\rightarrow\\texttt\{research\\\_proposal\.md\}\.\(3\)Each artifact has a distinct role\.claim\_audit\.mdis the evidence ledger, grounding claims in sources and recording limitations and implications\.synthesis\.mdcompresses audited evidence into cross\-paper tensions, mechanisms, boundary conditions, and research opportunities\.idea\_candidates\.mdrecords candidate directions, rejected alternatives, and the selection logic behind the winning idea\.research\_proposal\.mdexpands that idea into an executable proposal with motivation, method, evaluation, risks, and implementation details\.

## 4IdeaTrail Construction: Reverse\-Synthesized Research Trajectories

This section describes howIdeaTrailconstructs research trajectories\. As shown in[Figure˜2](https://arxiv.org/html/2607.10144#S4.F2), the pipeline first derives Advisor\-only constraints and anchor artifacts from human\-selected final research artifacts, then asks the Generator to produce a chronological trajectory with tool calls, observations, and artifact edits\. The Advisor verifies that the generated process remains grounded, causally ordered, natural, and free of leakage from hidden targets\.

![Refer to caption](https://arxiv.org/html/2607.10144v1/x1.png)Figure 2:Overview of theIdeaTraildata\-generation pipeline\.### 4\.1The Hindsight Problem

Letppdenote a final research proposal and letτ\\taudenote a plausible forward trajectory that could have produced it\. A naive inverse\-generation setup would ask a Generator to solve

p→τ\.p\\rightarrow\\tau\.\(4\)
This inverse problem is underdetermined: many different trajectories can lead to the same final proposal\. If the Generator is directly conditioned on the ground\-truth proposal, early turns can become superficially plausible but secretly depend on the final method, benchmark, terminology, or decision rule\. The resulting data may contain target leakage, unnatural search behavior, and overly linear reasoning\.IdeaTrailinstead separates Advisor\-side constraint construction from Generator\-side trajectory synthesis:

p→oadv→a⋆→τ^gen,p\\rightarrow o\_\{\\mathrm\{adv\}\}\\rightarrow a^\{\\star\}\\rightarrow\\hat\{\\tau\}\_\{\\mathrm\{gen\}\},\(5\)
whereoadvo\_\{\\mathrm\{adv\}\}is Advisor\-only oracle context,a⋆a^\{\\star\}is a set of anchor artifacts, andτ^gen\\hat\{\\tau\}\_\{\\mathrm\{gen\}\}is the generated trajectory\. The Advisor uses the oracle context and anchors to constrain and verify the target, while the Generator produces the visible trajectory in chronological order\.

### 4\.2Advisor Context, Anchors, and Trajectories

The Advisor\-only context is extracted from the final proposal\. It includes final atoms, artifact atoms, causal dependencies, and leakage locks\. Final atoms summarize the thesis, method, evaluation, decision rule, novelty, risks, and other proposal\-critical components\. Artifact atoms specify what the intermediate artifacts should eventually discover\. Leakage locks specify which final atoms may not appear before a given stage\.

Anchor artifacts are deterministic targets built from the proposal seed and Advisor\-only context\. They include anchor versions of the claim audit, synthesis, idea candidates, and proposal\. These anchors are not shown to the Generator as trajectory inputs; they are used by the Advisor and verifiers as target constraints\. The generated trajectory is still allowed to contain local uncertainty, detours, rejected ideas, and incremental edits\.

### 4\.3Synthesis Objective

The generated trajectory should satisfy four constraints:

Convergence​\(τ^gen,a⋆\)\\displaystyle\\text\{Convergence\}\(\\hat\{\\tau\}\_\{\\mathrm\{gen\}\},a^\{\\star\}\)≥γ,\\displaystyle\\geq\\gamma,\(6\)Leakage​\(τ^gen,oadv\)\\displaystyle\\text\{Leakage\}\(\\hat\{\\tau\}\_\{\\mathrm\{gen\}\},o\_\{\\mathrm\{adv\}\}\)=0,\\displaystyle=0,\(7\)Grounding​\(τ^gen\)\\displaystyle\\text\{Grounding\}\(\\hat\{\\tau\}\_\{\\mathrm\{gen\}\}\)≥η,\\displaystyle\\geq\\eta,\(8\)Naturalness​\(τ^gen\)\\displaystyle\\text\{Naturalness\}\(\\hat\{\\tau\}\_\{\\mathrm\{gen\}\}\)≥ν\.\\displaystyle\\geq\\nu\.\(9\)
Convergence means the final idea or proposal aligns with the anchor’s core mechanism\. Leakage means Advisor\-only terms or final conclusions do not appear too early\. Grounding means claims are supported by observed sources\. Naturalness means the trajectory contains realistic uncertainty, reading, revision, and staged convergence\.

### 4\.4Proposal Ingestion

The pipeline begins withresearch\_proposal\.md\. The ingestion step parses the proposal into a structured proposal seed\. This step is conservative: it extracts information already present in the proposal and avoids adding missing details\. The proposal seed is the factual base for downstream reverse planning\. A simplified seed schema is shown below\.

\{

"proposal\_id":"case\_id",

"research\_question":"\.\.\.",

"thesis":"\.\.\.",

"method":"\.\.\.",

"evaluation":"\.\.\.",

"baselines":\[\.\.\.\],

"risks":\[\.\.\.\],

"novelty\_claim":"\.\.\.",

"parse\_quality":"high\|medium\|low"

\}

### 4\.5Visible Direction Generation

The visible direction converts the initial research question and allowed context into a natural user message\. It gives the agent enough information to begin research, but it excludes proposal\-specific method names, final titles, benchmark names, and internal pipeline terms\. An exact\-term leakage check is applied before the visible direction is accepted\.

### 4\.6Anchor Artifact Construction

The anchor builder creates four target artifacts:

a⋆=\(aclaim⋆,asynth⋆,aidea⋆,aproposal⋆\)\.a^\{\\star\}=\(a^\{\\star\}\_\{\\text\{claim\}\},a^\{\\star\}\_\{\\text\{synth\}\},a^\{\\star\}\_\{\\text\{idea\}\},a^\{\\star\}\_\{\\text\{proposal\}\}\)\.\(10\)
The anchor claim audit is grounded in searched or parsed papers\. The anchor synthesis expands the oracle’s synthesis atoms into problem levels, evidence base, mechanism hypotheses, contradictions, boundaries, and candidate directions\. The anchor idea artifact records the winning idea\. The anchor proposal is the most faithful target version of the original proposal\.

### 4\.7High\-Level Milestone Planning

Given the hidden oracle, anchors, and a turn budget, the high\-level planner creates milestones\. A milestone specifies what should have been achieved by a certain point in the trajectory\. For example, a 50\-turn trajectory may allocate early turns to broad evidence gathering, middle turns to claim audit and synthesis, and late turns to idea selection and proposal writing\.

### 4\.8Turn Generation and Public Serialization

The trajectory generator produces one turn at a time, but the public release does not serialize turns as separate JSONL records\. Instead, each JSONL line contains one complete trajectory\. Turn\-level assistant messages, tool calls, tool results, and artifact\-edit actions are retained inside the orderedmessagesarray\. The top\-level release schema is illustrated below; underscore\-prefixed fields are release metadata and may be absent\.

\{

"sample\_id":"\.\.\.",

"messages":\[\.\.\.\],

"tools":\[\.\.\.\],

"parallel\_tool\_calls":true,

"\_src":"\.\.\.",

"\_grade":"A\|B\|C\|None",

"\_naturalness":\.\.\.,

"\_proposal\_synth":true,

"\_proposal\_path":"\.\.\."

\}

This schema is the public trajectory\-level representation\. Internal synthesis\-time state may additionally track turn identifiers, stages, milestones, or artifact deltas, but those fields do not constitute the schema of the public release\. In particular,\_grademay also be missing, and the literal string"None"is distinct from a missing key\.

### 4\.9Verifier\-Guided Retry

The verifier checks each turn or milestone segment\. It does not generate new trajectory content\. It returns pass or retry instructions\. Typical retry triggers include forbidden\-term leakage, creating an artifact too early, using a paper title before it appears in search observations, fabricating a URL, or drifting away from the target problem space\.

## 5Dataset Format

### 5\.1Trajectory Types

Not every trajectory inIdeaTrailis extended all the way to a research proposal\. After the Generator producesidea\_candidates\.md, an Advisor\-side judge checks whether the selected idea has converged to the same core problem, mechanism, and research direction as the ground\-truth target\. Because Generator–Advisor synthesis still allows local uncertainty and exploratory variation, some generated ideas may be useful but slightly shifted from the target\. These cases are retained as idea\-level trajectories and are not further expanded intoresearch\_proposal\.md\.

The dataset therefore records two endpoint types\. Inidea\-onlycases, the system prompt guides the Generator only through evidence gathering, claim audit, synthesis, and idea candidate selection\. Inproposal\-extensioncases, the idea passes the convergence judge and the prompt continues to proposal writing\. This distinction prevents weakly aligned ideas from being forced into ground\-truth proposals while still preserving useful ideation supervision\. The release contains 688 proposal\-extension trajectories, marked by\_proposal\_synth=true; these records also contain\_proposal\_path\. The remaining 482 idea\-level trajectories omit both fields\. Thus, endpoint type uses a true\-versus\-absent encoding rather than a conventional Boolean true/false field\.

### 5\.2Value\-Tier Labels

The dataset includes value\-tier labels to support weighted training\. The current trajectories have not yet undergone aggressive post\-hoc filtering, and many turns contain long reasoning traces or other low\-information intermediate text\. Treating every turn as equally valuable could over\-train agents on redundant reasoning styles and dilute the supervision from turns that actually drive evidence use, idea selection, and proposal construction\. The label is therefore not merely a measure of surface text quality; it estimates each turn’s contribution to the final idea or proposal\.

- •High:turns that diagnose root causes, introduce nontrivial mechanisms, select or revise the winning idea, formalize the proposal method, or fix final proposal quality\.
- •Normal:useful research work such as reading, ordinary claim auditing, artifact drafting, and evidence consolidation\.
- •Low:mechanical, redundant, shallow, or formatting\-heavy turns\.

## 6Quality Control Protocol

### 6\.1One\-Vote Failure Conditions

A trajectory is rejected or routed to repair if it triggers any one\-vote failure condition:

- •Oracle leakage:internal terms or final conclusions appear too early\.
- •Title pre\-knowledge:a complete paper title is searched before it was observed\.
- •Fabricated evidence:a claim, result, citation, or paper conclusion is invented or misattributed\.
- •Fake URL:a scraper URL is not derived from a prior search observation\.
- •Target drift:the final idea or proposal moves outside the intended problem space or core mechanism\.

### 6\.2Trajectory Rubric

The main trajectory rubric uses the following five dimensions\.

Table 1:Trajectory quality rubric\.
### 6\.3Artifact Metrics

Artifact\-level metrics include grounding rate, traceability gap rate, proposal recoverability, leakage score, and artifact incrementality\. Artifact incrementality measures whether files emerge through skeleton, fill, revise, and finalize operations rather than being written in one unrealistic block\.

## 7Dataset Statistics

### 7\.1Counting Protocol

IdeaTrailcontainsN=1,170N=1\{,\}170long\-horizon trajectories for scientific ideation and research\-proposal generation\. Each JSONL record has a uniquesample\_id, a multi\-turnmessagessequence, seven tool definitions, and release metadata such as\_srcand\_grade\.

Three units are kept separate throughout this section: a trajectory is one JSONL record, a message is one role\-tagged conversational event, and a tool call is one function invocation emitted inside an assistant message\. A single assistant message may therefore contribute several tool calls\.

For trajectoryii, letmim\_\{i\}be its number of messages,titextt\_\{i\}^\{\\mathrm\{text\}\}its message\-text token count, andtijsont\_\{i\}^\{\\mathrm\{json\}\}its full\-record token count\. Means are computed as

x¯=1N​∑i=1Nxi,xi∈\{mi,titext,tijson\}\.\\bar\{x\}=\\frac\{1\}\{N\}\\sum\_\{i=1\}^\{N\}x\_\{i\},\\qquad x\_\{i\}\\in\\\{m\_\{i\},t\_\{i\}^\{\\mathrm\{text\}\},t\_\{i\}^\{\\mathrm\{json\}\}\\\}\.\(11\)
Text\-field counts concatenate all string\-valuedcontentandreasoning\_contentfields in message order with one newline between fields\. Full\-record counts encode the exact decompressed JSONL line without its trailing newline\.

Both views use theo200k\_basetokenizer\. Strings resembling special tokens are encoded as ordinary text\. Chat templates, packing, and added control tokens can increase the effective training length beyond these corpus\-level counts\.

### 7\.2Scale and Integrity

All 1,170 records parse successfully and all identifiers are unique\. Each trajectory begins with a system message followed by its only user task\. The parallel\-call flag is true in every record, and all records expose the same seven tool definitions\.

Table 2:Corpus scale, release size, and integrity checks\. Decimal MB and binary MiB are reported separately\.The case count is moderate, while each case contains a deep research process\. Consequently, the corpus contributes 92\.19M message\-text tokens and 135\.98M serialized\-JSON tokens despite having only 1,170 training examples\.

### 7\.3Long\-Horizon Context

Table[3](https://arxiv.org/html/2607.10144#S7.T3)reports exact distribution summaries, and Figure[3](https://arxiv.org/html/2607.10144#S7.F3)shows their empirical cumulative distributions\. Percentiles use NumPy’s linear interpolation rule\.

Table 3:Trajectory\-length distributions under message\-only and full\-record token accounting\. The serialized view includes keys, metadata, tool schemas, arguments, and punctuation\.![Refer to caption](https://arxiv.org/html/2607.10144v1/x2.png)Figure 3:Empirical cumulative distributions of trajectory depth\. Dashed colored lines mark medians, dotted gray lines mark P95, and the red reference in panel \(c\) marks 256k tokens\.Only 17 trajectories \(1\.5%\) fit within 64k tokens under full\-record accounting\. In contrast, 1,153 trajectories \(98\.5%\) exceed 64k, 347 \(29\.7%\) exceed 128k, and 27 \(2\.3%\) exceed 192k\.

The maximum serialized record reaches 255,865 tokens\. This upper tail makes truncation, sequence packing, attention memory, and loss masking central design choices rather than incidental preprocessing details\.

Text\-only training sees a shorter distribution: 351 trajectories \(30\.0%\) fit within 64k text tokens, while 66 \(5\.6%\) exceed 128k\. Reporting only the text view therefore understates the context required by schema\-preserving agent training\.

### 7\.4Message and Text Composition

Tool observations dominate both event frequency and text mass\. Tool messages contribute 122,154 of 163,057 messages \(74\.9%\), followed by assistant messages at 23\.7%; system and user messages each contribute 0\.7%\.

![Refer to caption](https://arxiv.org/html/2607.10144v1/x3.png)Figure 4:Corpus composition by \(a\) message role and \(b\) character source\. Shares in panel \(b\) are computed over all string\-valued message text fields\.At the character level, tool observations contribute 293\.43M characters \(76\.5%\)\. Assistant reasoning contributes another 82\.11M \(21\.4%\)\. Together they account for 97\.8% of the recorded text\.

Visible assistant content contributes only 0\.42M characters \(0\.1%\)\. The primary supervision signal is therefore carried by evidence, intermediate reasoning, and state\-changing actions rather than by short final responses\.

### 7\.5Tool\-Use Structure

For toolff, its call share is

pf=nf∑f′nf′,p\_\{f\}=\\frac\{n\_\{f\}\}\{\\sum\_\{f^\{\\prime\}\}n\_\{f^\{\\prime\}\}\},\(12\)wherenfn\_\{f\}is the number of calls toff\. Reading consists ofView; retrieval combinesWebSearchandScraper; authoring combinesWriteandEdit; local discovery combinesGlobandGrep\.

![Refer to caption](https://arxiv.org/html/2607.10144v1/x4.png)Figure 5:Agent tool behavior\. Panel \(a\) reports call volume with tool\-family colors\. Panel \(b\) reports the number of calls emitted by each tool\-calling assistant message\.Table 4:Tool\-call frequency and observation length\. Each tool observation is matched to the immediately preceding assistant call batch usingtool\_call\_id\.Reading and retrieval account for 62,485 \(51\.2%\) and 44,608 \(36\.5%\) calls, respectively\. Their combined share is 87\.7%, compared with 9\.5% for authoring/editing and 2\.8% for local discovery\.

Among 38,563 assistant messages, 37,361 \(96\.9%\) emit at least one tool call\. The fanout mean is 3\.27 calls, the median is 3, P95 is 5, and the maximum is 13\. Most assistant turns therefore execute a small batch of coordinated actions before receiving observations\.

Tool\-call IDs have batch\-local scope\. Reuse occurs in 723 trajectories, with 8,464 call occurrences beyond the first use of an ID and a maximum of 36 uses for one ID\. A trajectory\-global ID map can silently attach observations to the wrong calls and distort per\-tool observation statistics\.

### 7\.6Supervision and Release Metadata

Every assistant message carries non\-empty reasoning text\. Only 514 \(1\.3%\) have non\-empty visible content\. Among turn\-level tier labels, 17,679 are normal \(45\.8%\), 13,109 are high \(34\.0%\), and 6,202 are low \(16\.1%\); the label is absent for 1,573 messages \(4\.1%\)\.

Trajectory\-level\_gradecounts are 754A, 44B, 31C, 228 literal strings"None", and 113 missing keys\. The explicit A/B subset therefore contains 798 trajectories \(68\.2%\)\.

The source\-grade relation is deterministic in this release:v5contains 94 A and 8 B trajectories;rp23contains 228 literal"None"and 113 missing grades;rp4contains 660 A, 36 B, and 31 C trajectories\. These values are release\-pipeline metadata and do not define a portable ordinal quality scale\.

### 7\.7Topic and Temporal Coverage

Topic identifiers are extracted from structured tool paths rooted in the release topic directory\. The 1,170 trajectories cover 963 topics; 840 topics occur once, and the ten most frequent topics account for only 70 trajectories \(6\.0%\)\.

The topic entropy is 9\.745 bits\. Normalizing by the maximum entropy for 963 observed categories gives

Hnorm=−∑jpj​log2⁡pjlog2⁡963=0\.983,H\_\{\\mathrm\{norm\}\}=\\frac\{\-\\sum\_\{j\}p\_\{j\}\\log\_\{2\}p\_\{j\}\}\{\\log\_\{2\}963\}=0\.983,\(13\)which confirms a broad long tail rather than concentration in a few dominant topics\.

![Refer to caption](https://arxiv.org/html/2607.10144v1/x5.png)Figure 6:Coverage and release metadata\. Panel \(a\) shows the topic rank\-frequency curve, panel \(b\) summarizes the first valid ISO date in each system prompt, and panel \(c\) shows source\-conditioned grade composition\.The most frequent topic, single\-image 3D reconstruction, appears in 10 trajectories\. Two topics have 8 trajectories, two have 7, and five have 6; the remaining mass is distributed across the long tail\.

Prompt dates are parseable for 1,100 trajectories \(94\.0%\), spanning 196 unique dates from 2018\-12\-18 to 2026\-01\-16\. Of these, 1,019 fall in 2025\. Prompt dates describe the stated evidence cutoff context and should not be interpreted as publication or generation timestamps\.

Topic and temporal leakage require separate controls\. A topic\-disjoint split can still share cutoff periods, while a temporally disjoint split can retain near\-duplicate research themes\.

### 7\.8Format and Linkage Audit

Source\-grade matrix

Format and linkage checks CheckValueTop\-level / message schema variants5 / 9Records with proposal metadata688Records with\_reclaimmetadata31Assistant\-final / tool\-final trajectories1,168 / 2Unmatched tool observations0Calls with empty function name11Trajectories reusing tool\-call IDs723Repeated call\-ID occurrences8,464Maximum uses of one call ID36Messages with\_new=true16,522Trajectories with special\-token\-like strings42Unique special\-token\-like strings89

Table 5:Release metadata and format\-level audit\. Missing\_grademeans the key is absent; literal"None"remains a string\.The 688 proposal\-path values are sanitized relative construction paths and serve as auxiliary provenance rather than semantic targets\. The 31\_reclaimvalues and 113 missing\-grade records likewise require explicit missing\-value handling\.

Two trajectories terminate with a tool message, and 11 tool calls have an empty function name\. Training code that assumes assistant\-final samples or validates only null tool names will mishandle these cases\.

Overall,IdeaTrailis characterized by three coupled properties: long context, dense tool interaction, and broad topic coverage\. A median trajectory contains 134 messages and 110,815 serialized tokens; 87\.7% of calls retrieve or inspect evidence; and 87\.2% of topic identifiers occur exactly once\.

These properties favor training and evaluation protocols that preserve tool linkage, account for schema tokens, and control topic and temporal leakage\. They also make aggregate sample count alone a poor proxy for the corpus’s computational and supervisory scale\.

## 8Limitations

This release should be viewed as an initial and necessarily incomplete corpus rather than a fully cleaned training set\. Generating and verifying long\-horizon research trajectories is computationally and financially expensive, which limits both the number of trajectories and the amount of post\-processing that can be applied\. The current data have not undergone systematic turn\-level filtering beyond the existing quality\-control pipeline\. Consequently, some turns—including turns labeled with a low value tier—still contain lengthy reasoning, repeated planning, mechanical tool interactions, or other low\-information text\. Value\-tier labels make such content easier to filter or down\-weight, but they do not remove the noise, and the labels themselves may be imperfect\.

The corpus also has limited process diversity\. Most trajectories were produced with the same agent harness, tool interface, stage definitions, artifact templates, and Generator–Advisor protocol\. This consistency improves auditability, but it can introduce common stylistic and behavioral patterns\. An agent trained primarily on this corpus may therefore overfit to the current harness and generalize poorly to different tool APIs, interaction protocols, research domains, shorter workflows, or human–agent collaboration settings\. Moreover, 1,170 trajectories cover only a small fraction of the possible research questions and workflows; long trajectories do not substitute for a larger number of independent research processes\.

Reverse synthesis introduces an additional source of bias\. Although the final proposal and Advisor\-only constraints are hidden from the Generator, the Advisor and anchor artifacts are still derived from a known endpoint\. Generated trajectories may therefore be more coherent and convergent than research in the wild, underrepresent unsuccessful exploration, or inherit assumptions embedded in the selected final artifact\. Retaining idea\-only trajectories avoids forcing every partially shifted idea into a proposal, but it does not eliminate this hindsight bias\.

Finally, automated verification cannot fully establish scientific correctness, naturalness, or downstream training utility\. Search results and web content may be incomplete or change over time; paper interpretations may remain shallow; and selection based on human\-curated artifacts and optional researcher portraits may introduce domain, author, and venue biases\. The current report also does not establish through large\-scale human evaluation or downstream training experiments that value\-tier weighting or the synthesized trajectories improve research agents across settings\.

We therefore view the main value of this work not as presenting a definitive or exhaustive dataset, but as providing a concrete reference recipe for synthesizing research\-process supervision: start from high\-quality final artifacts, construct hidden Advisor constraints and intermediate anchors, generate the visible tool\-using trajectory forward, and verify its grounding and causal order\. Future releases should expand the number of trajectories, models, harnesses, tools, and research domains; apply stronger post\-processing and human auditing; and evaluate transfer to independently implemented research agents\.

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