TermiGen: Terminal Agent vs. Terminology Generation

• TermiGen is a term that refers to two distinct systems: one for terminal-agent training with verifiable, error-injected Docker environments and another for retrieval-augmented terminology drafting.
• The terminal-agent system employs a two-phase pipeline that generates functionally valid tasks and detailed error-recovery trajectories to enhance model robustness.
• The terminology-generation blueprint uses retrieval methods and language models to extract, cite, and generate precise definitions in legal, technical, and scientific contexts.

Searching arXiv for the cited TermiGen and LexDrafter papers to ground the article in current preprints. TermiGen is a name used for two distinct systems in recent arXiv literature. In one usage, it denotes an end-to-end pipeline for terminal-agent training that synthesizes verifiable Docker-based environments and robust trajectories with active error injection, culminating in the model TermiGen-Qwen2.5-Coder-32B for TerminalBench evaluation (Zhu et al., 6 Feb 2026). In another usage, it denotes a blueprint for a “Terminology-Generation” system inspired by LexDrafter, in which retrieval augmented generation is used to draft or cite definitions for terms in legal, technical, or scientific documents (Chouhan et al., 2024). The shared label obscures a substantive divergence of purpose: one system addresses executable terminal-task learning, while the other addresses terminology drafting and definition management.

1. Nomenclature and disambiguation

The two systems share a name but not a domain, training objective, or evaluation regime.

Usage of “TermiGen”	Source	Core characterization
Terminal-agent TermiGen	"TermiGen: High-Fidelity Environment and Robust Trajectory Synthesis for Terminal Agents" (Zhu et al., 6 Feb 2026)	Two-phase pipeline for environment synthesis and error-injection trajectory generation
Terminology-generation TermiGen	Blueprint inspired by "LexDrafter: Terminology Drafting for Legislative Documents using Retrieval Augmented Generation" (Chouhan et al., 2024)	Retrieval-then-generation framework for citing or drafting definitions

This name collision is the principal source of ambiguity. A common misconception is to treat “TermiGen” as a single framework spanning both terminal interaction and terminology drafting. The supplied literature does not support that reading. Instead, it documents two separate constructs: one centered on terminal agents and executable Docker environments, the other centered on corpora, definition extraction, and RAG-assisted drafting.

2. Terminal-agent TermiGen: objective and overall design

The terminal-agent formulation begins from two stated bottlenecks. First, high-fidelity, executable training environments are scarce: environments synthesized from real-world repositories are “not diverse and scalable,” while trajectories synthesized by LLMs “suffer from hallucinations.” Second, standard instruction tuning produces a distributional mismatch because expert trajectories are rarely imperfect, so student models do not learn to recover from the simple runtime failures that smaller models often generate at inference time (Zhu et al., 6 Feb 2026).

TermiGen addresses these bottlenecks through a two-phase pipeline. Phase I performs automatic generation of functionally valid tasks and Docker containers via an iterative multi-agent refinement loop. Phase II performs trajectory synthesis with a Generator–Critic protocol that actively injects errors during collection, thereby constructing data rich in “error-correction cycles.” The design target is not merely successful expert completion, but explicit coverage of the loop “action → error feedback → diagnosis → repair.”

The architecture is organized around specialized agents. In Phase I, these include a Task Proposal Agent, Task Evaluator Agent, File Planner, Content Constructor, Env Agent, Unit-Test Generator, and Judge Agent. In Phase II, they include a StepGeneratorAgent and CriticAgent operating over executable container state. This decomposition makes the pipeline both constructive and discriminative: artifacts are generated, then filtered or refined until they satisfy buildability, task validity, and test determinism constraints.

A plausible implication is that the framework is less a single model than a data-generation and verification stack for downstream SFT. That interpretation is consistent with the reported workflow in which generated data are used to fine-tune Qwen-2.5-Coder-32B and Qwen3-32B rather than to replace model training directly.

3. Verifiable environment synthesis

Phase I is an iterative multi-agent environment-synthesis procedure that begins from a taxonomy $T$ of categories and a target number of tasks $N$. High-level seeds are sampled, task proposals are generated, and those proposals are scored by the TaskEvaluatorAgent on three 1–5 scales: Environment Complexity, Data Generatability, and Verification Determinism. Feedback then drives up to three rounds of refinement (Zhu et al., 6 Feb 2026).

Once a task description is accepted, the pipeline decomposes textual requirements into a directory structure, file contents, and a Dockerfile. The FilePlannerAgent produces a plan, the ContentConstructorAgent generates scripts and configuration files, and the EnvAgent produces a Dockerfile that installs the required system and language dependencies. The Docker build process itself is embedded in a repair loop of up to five iterations: if docker_build(dockerfile) fails, stderr is captured and the EnvAgent attempts to fix the Dockerfile.

Unit tests are generated after environment construction. The UnitTestGeneratorAgent proposes tests, and the JudgeAgent validates whether those tests distinguish correct solutions from incorrect attempts, cover edge cases, and run without errors. Refinement continues until the tests are accepted or the refinement budget is exhausted. A task is retained only if the build succeeds and the tests are valid.

The stated outcome of Phase I is stringent: “100% of the environments are functionally valid” and each task is paired with deterministic unit tests (Zhu et al., 6 Feb 2026). That guarantee is central to the paper’s contrast with “simulation” methods that hallucinate execution traces. Within the paper’s own framing, environment verifiability is not a peripheral engineering convenience but the foundation that makes grounded feedback available for trajectory synthesis and benchmark evaluation.

4. Generator–Critic trajectory synthesis and active error injection

Phase II constructs trajectories over verified environments using active error injection. At each time step $t$, an intent variable is sampled as $I_t \sim \mathrm{Bernoulli}(\epsilon)$ with $\epsilon = 0.2$, so that $P(I_t=\text{error})=\epsilon$ and $P(I_t=\text{correct})=1-\epsilon$ (Zhu et al., 6 Feb 2026). The trajectory generator therefore alternates stochastically between producing correct steps and producing plausible mistakes.

The StepGeneratorAgent is conditioned on $(s_t, I_t)$. When $I_t=\text{error}$, it must produce a plausible domain-specific error drawn from one of five categories: Analysis, Command, Hallucination, Requirement Violation, and Verification Failure. The CriticAgent validates the step for syntactic plausibility and checks whether an error-intended step yields informative stderr or whether a correct-intended step advances the task state, for example by reducing test failures. Low-quality steps are regenerated.

The error-recovery coupling is explicit. When the next sampled intent $I_{t+1}$ is “correct” after an error step, the generator is forced to produce a corrective action using the observed stderr feedback. This forces the model to encounter and solve the specific failure mode it has just induced. Over many steps, the resulting trajectories contain multi-turn chains of error, diagnosis, repair, and re-execution rather than clean demonstrations alone.

The resulting dataset comprises “3 500+ verified Docker environments” across 11 categories, “3 291 trajectories” averaging 25.5 steps and approximately 8,722 tokens each, and “420 distinct command-line tools across 16 functional domains,” ranging from basic utilities to security forensics and HPC (Zhu et al., 6 Feb 2026). Because the injection rate is fixed at $N$0, each trajectory is designed to contain multiple realistic errors and corresponding corrections. The Coq example given in the paper illustrates the pattern: an injected compilation failure in a proof task is followed by diagnosis of the equality issue, insertion of Require Import Nat. and the appropriate rewrite lemmas, and successful recompilation.

This training distribution differs materially from standard expert-only traces. The paper’s own interpretation is that such trajectories teach the model how to interpret stderr and adapt, behavior that standard demonstrations do not expose.

5. Evaluation, ablations, and limitations

Evaluation is performed on TerminalBench 1.0. Each synthesized agent is run inside a fresh Docker container, its commands are executed, and a provided test.sh script is run afterward. A pass is recorded only if all unit tests succeed. Pass rate is defined as

$N$1

The reported metric is Avg Pass@1 over three independent runs, with temperature $N$2 (Zhu et al., 6 Feb 2026).

The student models are Qwen-2.5-Coder-32B and Qwen3-32B, both fine-tuned via full-parameter supervised fine-tuning for 5 epochs with sequence length 20 000. The principal reported result is that TermiGen-Qwen2.5-32B reaches a pass rate of $N$3, while TermiGen-Qwen3-32B reaches $N$4. Among comparison points listed in the paper, Reptile records $N$5 and TerminalAgent records $N$6. The proprietary ceiling in the table is Apex2 at $N$7, and the paper states that TermiGen-Qwen2.5-32B exceeds o4-mini at $N$8 by 11.3 points (Zhu et al., 6 Feb 2026).

The ablations are designed to isolate the contribution of grounding, error injection, and imperfect data retention. For verifiable versus simulated feedback on $N$9, simulation yields $t$0 while TermiGen real yields $t$1, a $t$2 point gain; the accompanying analysis states that approximately $t$3 of synthetic observations hallucinate errors and mis-route diagnostics. For error injection versus standard SFT on $t$4, standard training without active injections yields $t$5, while TermiGen with $t$6 yields $t$7, a $t$8 point gain. For negative-trajectory inclusion, filtering to only perfect runs ($t$9) yields $I_t \sim \mathrm{Bernoulli}(\epsilon)$0, allowing runs with at least $I_t \sim \mathrm{Bernoulli}(\epsilon)$1 unit-test pass rate yields $I_t \sim \mathrm{Bernoulli}(\epsilon)$2, and retaining all runs ($I_t \sim \mathrm{Bernoulli}(\epsilon)$3) yields $I_t \sim \mathrm{Bernoulli}(\epsilon)$4. The reported interpretation is that even ultimately failing runs contain informative local error-recovery supervision.

The stated limitations are also specific. The system uses only supervised fine-tuning; reinforcement learning on deterministic tests is proposed as future work. The agent implementation is minimal, with no long-term memory or retrieval. All environments are synthetic and isolated, so adaptation to open-ended production clusters or stochastic networks remains future work (Zhu et al., 6 Feb 2026). These caveats are essential when interpreting the reported open-weight state-of-the-art: the gains are established in a controlled, synthetic, Dockerized regime rather than in unrestricted operational settings.

6. Terminology-generation TermiGen: LexDrafter-derived retrieval and drafting

A separate blueprint uses the name TermiGen for a “Terminology-Generation” system inspired by LexDrafter. Its input is a draft document; its output is either a cited existing definition or a newly generated concise definition obtained through retrieval augmented generation (Chouhan et al., 2024). The workflow is divided into an offline corpus-building pipeline and an online runtime pipeline.

The offline branch performs document acquisition, normalization, document structuring, definition extraction, and citation resolution. Documents $I_t \sim \mathrm{Bernoulli}(\epsilon)$5 are decomposed into sections, paragraphs, and sentences, while preserving order. “Definitions” sections are then located by headings such as “Article 1 – Definitions” or “Terminology,” and regex or linguistic patterns are used to extract $I_t \sim \mathrm{Bernoulli}(\epsilon)$6 pairs. The resulting definition elements are represented as

$I_t \sim \mathrm{Bernoulli}(\epsilon)$7

where $I_t \sim \mathrm{Bernoulli}(\epsilon)$8 is a unique identifier, $I_t \sim \mathrm{Bernoulli}(\epsilon)$9 is the defined term, $\epsilon = 0.2$0 is the definition text, and $\epsilon = 0.2$1 is a list of references to other definition elements if the definition is dynamic. A CiteResolver populates reference targets for externally cited definitions.

The online branch begins with optional term detection, either by automatic candidate selection via POS-patterns or by user selection. A TermRetriever then queries the definition index for exact or fuzzy matches. If an existing static definition is found, it is presented directly, optionally ranked by domain tags. If no suitable definition is found, or if a fresh definition is requested, the system forwards the query to a RAG component. The retriever uses a lightweight lexical mechanism such as BM25 to fetch top-$\epsilon = 0.2$2 fragments in which the term appears, then re-ranks by term frequency and selects top-$\epsilon = 0.2$3 fragments, for example $\epsilon = 0.2$4. The generator assembles a prompt instructing the LLM to define the term in 25–45 words and returns JSON of the form {term: T, definition: …}.

The mathematical description includes dense and lexical retrieval components. Definitions and queries can be embedded as $\epsilon = 0.2$5 and $\epsilon = 0.2$6, with dense retrieval scored by $\epsilon = 0.2$7. Lexical retrieval is represented as $\epsilon = 0.2$8 or $\epsilon = 0.2$9. Generation is trained with the standard conditional language-model objective

$P(I_t=\text{error})=\epsilon$0

The blueprint specifies Sentence-Transformer models such as all-MiniLM-L6 for dense retrieval, BM25 via Elasticsearch or OpenSearch for lexical retrieval, and LLMs such as Vicuna-7b or LLaMA-2-7b for generation. It also provides decoding parameters—temperature $P(I_t=\text{error})=\epsilon$1, top_k $P(I_t=\text{error})=\epsilon$2, top_p $P(I_t=\text{error})=\epsilon$3, repetition_penalty $P(I_t=\text{error})=\epsilon$4, max_context $P(I_t=\text{error})=\epsilon$5—and evaluation metrics including BLEU-$P(I_t=\text{error})=\epsilon$6, BERTScore, and BLEURT. Sample reported results on the EU Energy domain compare Vicuna-7b and LLaMA-2-7b with BLEU-2 of approximately $P(I_t=\text{error})=\epsilon$7, BERTScore of approximately $P(I_t=\text{error})=\epsilon$8, and BLEURT of approximately $P(I_t=\text{error})=\epsilon$9. The broader design pattern is modular: build a searchable definition and fragment corpus offline, then at draft time either cite or generate a definition conditioned on retrieved evidence.

Taken together, the two TermiGen usages illustrate how the same name has been attached to two unrelated but structurally modular research programs. One program synthesizes executable environments and resilient trajectories for terminal agents; the other synthesizes or retrieves terminology definitions for document drafting. Their commonality lies not in task domain but in pipeline design: both rely on staged decomposition, explicit quality-control loops, and strong dependence on retrieved or executed evidence rather than unconstrained free-form generation.