EnviroLLM: Integrating Environment in LLMs

• EnviroLLM is a research direction that integrates environmental contexts—ranging from software execution to physical ecosystems—directly into LLM architectures.
• It leverages quantitative benchmarks such as compile success rates, RMSE, energy consumption, and test-pass metrics to evaluate and optimize performance.
• The heterogeneous applications of EnviroLLM highlight its evolving nature, promoting future improvements in agent coordination, robustness, and transparency.

EnviroLLM is a label used in recent arXiv literature for several distinct classes of large-language-model systems that make “environment” a first-class object of modeling, interaction, or evaluation. In the current record, the term spans at least four technical meanings: software build and runtime environments in code migration, physical environmental ecosystems in spatial-temporal prediction, the resource and energy footprint of local AI deployment, and sustainability-oriented cognition or environmental-content processing in evaluation and classification pipelines (Li et al., 10 Feb 2026). This breadth implies that EnviroLLM is not yet a standardized architecture or benchmark family; rather, it denotes a heterogeneous research direction in which LLMs are coupled to environmental state, environmental data, or environmental criteria.

1. Terminological scope and research landscape

Recent work uses the EnviroLLM label across multiple problem settings. In "Environment-in-the-Loop: Rethinking Code Migration with LLM-based Agents" (Li et al., 10 Feb 2026), the relevant “environment” is the executable software setting that must be constructed, diagnosed, and revised together with migrated code. In "LITE: Modeling Environmental Ecosystems with Multimodal LLMs" (Li et al., 2024), the environment is a physical ecosystem represented through environmental variables over space and time. In "EnviroLLM: Resource Tracking and Optimization for Local AI" (Allen, 12 Dec 2025), the environment is the computational and energy context of local inference. In "Greener Than Humans? Environmental Attitudes in LLMs" (Kunkel et al., 1 Jun 2026), the emphasis is on environmental cognition, affect, behavioural recommendations, and governance. In "Evaluating the performance of state-of-the-art esg domain-specific pre-trained LLMs in text classification..." (Chung et al., 2024), an environmental-domain LLM appears as EnvLlama 2-Qlora for ESG text classification.

Paper	Meaning of “environment”	Primary task
(Li et al., 10 Feb 2026)	Build/runtime image, dependencies, configuration, logs	Code migration with automated environment interaction
(Li et al., 2024)	Environmental ecosystems and spatial-temporal variables	Prediction of streamflow, temperature, and N₂O emissions
(Allen, 12 Dec 2025)	Local AI resource usage and environmental impact	Tracking, benchmarking, and optimization
(Kunkel et al., 1 Jun 2026)	Sustainability-related attitudes and behaviour	Benchmarking environmental cognition and steerability
(Chung et al., 2024)	Environmental content within ESG disclosures	Binary text classification

A common misconception is that EnviroLLM refers only to sustainability-themed LLMs. The literature does not support such a narrow reading. One strand concerns sustainability and environmental semantics directly (Li et al., 2024, Kunkel et al., 1 Jun 2026, Chung et al., 2024), but another strand concerns operational environments for software evolution (Li et al., 10 Feb 2026) and resource accounting for local inference (Allen, 12 Dec 2025). This suggests that the unifying idea is not topic alone, but the explicit integration of environmental context into the objective, workflow, or evaluation.

2. Environment-in-the-loop software migration

In the software-engineering setting, EnviroLLM is a three-agent, closed-loop workflow composed of a Migration Agent (M-Agent), an Environment Agent (E-Agent), and a Testsuite Agent (T-Agent) (Li et al., 10 Feb 2026). The architecture uses the following labeled flows: source code and migration specification, $\mathcal{C}_{src}$ and $S_{mig}$; migration plan $\pi$; migrated code $\mathcal{C}_{mig}$; environment image $\mathcal{I}_{env}$; diagnostics $D_{env}$; feedback $F_{env}$; generated tests $\mathcal{T}_{gen}$; and test reports $R_{test}$. The central claim is that code migration and environment construction must be treated jointly rather than sequentially.

The code-migration task is formalized as

$$\mathcal{T}_{code}:\; (\mathcal{C}_{src}, S_{mig}) \;\longmapsto\; \mathcal{C}_{mig},$$

where $S_{mig}$0 is the set of input source files, $S_{mig}$1 is the migration spec, and $S_{mig}$2 is the mutated codebase candidate. The environment is modeled as

$S_{mig}$3

where $S_{mig}$4 is a codebase to build, $S_{mig}$5 is the manifest of dependencies, $S_{mig}$6 is a configuration profile, $S_{mig}$7 is the resulting runtime image, $S_{mig}$8 are captured build/runtime logs, and $S_{mig}$9 are execution results (Li et al., 10 Feb 2026).

The integrated objective couples code correctness and environment fidelity: $\pi$0 Here, $\pi$1, $\pi$2 penalizes runtime exceptions or crashes, $\pi$3 counts failing test cases, and $\pi$4 measures divergence between intended and actual dependency versions. The environment-drift term is further quantified as

$\pi$5

The agent decomposition is operational. M-Agent takes $\pi$6 as inputs and outputs $\pi$7, generating a plan, applying transformations, and inferring dependencies. E-Agent takes $\pi$8, bootstraps a base image, installs dependencies, builds and runs the migrated code, and returns $\pi$9. T-Agent takes $\mathcal{C}_{mig}$0, legacy tests $\mathcal{C}_{mig}$1, and environment results, generates new tests where old tests failed, repairs flaky or obsolete tests, and executes the merged testsuite in the environment (Li et al., 10 Feb 2026).

Evaluation is reported on EnvBench, comprising 2 000+ Python/JVM repos, and ExecutionAgent’s set of 50 repos in 5 languages. Migration success is measured by compilation success rate, runtime success rate, behavioral equivalence as test-pass rate, dependency-drift score, and setup time. Reported results are: Static-LLM (no agent), Compile $\mathcal{C}_{mig}$2, Runtime $\mathcal{C}_{mig}$3, Test Pass $\mathcal{C}_{mig}$4, Setup Time $\mathcal{C}_{mig}$5 s; ExecAgent, Compile $\mathcal{C}_{mig}$6, Runtime $\mathcal{C}_{mig}$7, Test Pass $\mathcal{C}_{mig}$8, Setup Time $\mathcal{C}_{mig}$9 s; EnviroLLM, Compile $\mathcal{I}_{env}$0, Runtime $\mathcal{I}_{env}$1, Test Pass $\mathcal{I}_{env}$2, Setup Time $\mathcal{I}_{env}$3 s (Li et al., 10 Feb 2026). In EnvBench’s Python subset, EnviroLLM reduced average setup time by $\mathcal{I}_{env}$4 and improved final test-pass rate by over $\mathcal{I}_{env}$5 pp. The paper also proposes a simple cooperative bandit approach in which each agent’s proposed change $\mathcal{I}_{env}$6 is scored by

$\mathcal{I}_{env}$7

and accepted only if $\mathcal{I}_{env}$8, with the explicit goal of breaking trivial retry loops.

3. Multimodal environmental ecosystems modeling

In LITE, an EnviroLLM is a multimodal LLM for environmental ecosystems modeling (Li et al., 2024). The system predicts environmental variables by transforming heterogeneous inputs into a semantic time-series modality and a temporal-trend image modality, then fusing both with a frozen LLM guided by domain instructions. The motivating problem is that conventional deep-learning approaches often struggle with incomplete features and distribution shifts in environmental datasets.

For each region $\mathcal{I}_{env}$9 at time $D_{env}$0, the raw inputs are physical drivers $D_{env}$1 and target history $D_{env}$2. The semantic modality $D_{env}$3 linearizes each $D_{env}$4 into key-value pairs and uses a frozen LLM to convert them into a natural-language sentence of approximately $D_{env}$5–$D_{env}$6 tokens; missing entries become “[MASK]”. The image modality $D_{env}$7 plots the past $D_{env}$8 days for each variable as small line graphs, stacks $D_{env}$9 variables side by side, and forms a single RGB or gray image (Li et al., 2024).

The semantic encoder $F_{env}$0 uses DistilBERT with $F_{env}$1 Transformer layers and hidden dimension $F_{env}$2. It encodes current, weekly, and yearly views, producing $F_{env}$3, $F_{env}$4, and $F_{env}$5. Missing-feature imputation is handled through a Sparse Mixture-of-Experts framework with $F_{env}$6 experts, each a two-layer feed-forward network of dimensions $F_{env}$7, with Top-$F_{env}$8 routing at $F_{env}$9. The gating and imputation are

$\mathcal{T}_{gen}$0

and

$\mathcal{T}_{gen}$1

After imputation, the text representation is concatenated as

$\mathcal{T}_{gen}$2

The vision encoder $\mathcal{T}_{gen}$3 is a tiny Swin-Transformer with patch size $\mathcal{T}_{gen}$4, window size $\mathcal{T}_{gen}$5, embedding dimension $\mathcal{T}_{gen}$6, and depths $\mathcal{T}_{gen}$7. Fusion is performed by a frozen Llama-2-7B decoder with $\mathcal{T}_{gen}$8 layers and hidden size $\mathcal{T}_{gen}$9, conditioned on a domain instruction $R_{test}$0. The final prediction is produced by a linear layer,

$R_{test}$1

Training minimizes a weighted sum of prediction loss, imputation loss, and $R_{test}$2 regularization: $R_{test}$3 with $R_{test}$4, $R_{test}$5, and $R_{test}$6 (Li et al., 2024).

The datasets are CRW-Temp, CRW-Flow, and AGR. CRW-Temp is daily, with $R_{test}$7 river segments and $R_{test}$8 temperature observations. CRW-Flow is daily, with $R_{test}$9 segments and $$\mathcal{T}_{code}:\; (\mathcal{C}_{src}, S_{mig}) \;\longmapsto\; \mathcal{C}_{mig},$$0 flow observations. AGR is hourly, with $$\mathcal{T}_{code}:\; (\mathcal{C}_{src}, S_{mig}) \;\longmapsto\; \mathcal{C}_{mig},$$1 chambers and $$\mathcal{T}_{code}:\; (\mathcal{C}_{src}, S_{mig}) \;\longmapsto\; \mathcal{C}_{mig},$$2 N$$\mathcal{T}_{code}:\; (\mathcal{C}_{src}, S_{mig}) \;\longmapsto\; \mathcal{C}_{mig},$$3O emission observations. Missing features are masked in text, missing points are linearly interpolated for the image, and each variable is z-normalized. Metrics are RMSE and MAE (Li et al., 2024).

Quantitatively, the abstract reports a $$\mathcal{T}_{code}:\; (\mathcal{C}_{src}, S_{mig}) \;\longmapsto\; \mathcal{C}_{mig},$$4 reduction in prediction error relative to the best baseline. The detailed results are RMSE $$\mathcal{T}_{code}:\; (\mathcal{C}_{src}, S_{mig}) \;\longmapsto\; \mathcal{C}_{mig},$$5 on CRW-Temp versus $$\mathcal{T}_{code}:\; (\mathcal{C}_{src}, S_{mig}) \;\longmapsto\; \mathcal{C}_{mig},$$6 best prior, RMSE $$\mathcal{T}_{code}:\; (\mathcal{C}_{src}, S_{mig}) \;\longmapsto\; \mathcal{C}_{mig},$$7 on CRW-Flow versus $$\mathcal{T}_{code}:\; (\mathcal{C}_{src}, S_{mig}) \;\longmapsto\; \mathcal{C}_{mig},$$8 prior, and RMSE $$\mathcal{T}_{code}:\; (\mathcal{C}_{src}, S_{mig}) \;\longmapsto\; \mathcal{C}_{mig},$$9 on AGR versus $S_{mig}$00 prior (Li et al., 2024). On CRW-Temp ablations, “text only” (+SMoE+multi-gran) gives RMSE $S_{mig}$01, “image only” $S_{mig}$02, “no LLM (linear head)” $S_{mig}$03, “no imputation” $S_{mig}$04, “SMoE→linear imputer” $S_{mig}$05, and “no multi-gran” $S_{mig}$06. Leave-sensors-out settings with $S_{mig}$07–$S_{mig}$08 features missing and OOD testing from $S_{mig}$09 training segments to $S_{mig}$10 test segments are reported as confirming strong robustness. This usage of EnviroLLM is therefore explicitly multimodal, instruction-guided, and designed for missing-data and distribution-shift regimes.

4. Local-AI resource tracking and optimization

In the local-inference setting, EnviroLLM is an open-source toolkit for tracking, benchmarking, and optimizing performance and energy consumption when running LLMs on personal devices (Allen, 12 Dec 2025). The system consists of a command-line interface, a backend processing engine, and a web dashboard. The CLI discovers running LLM processes by matching names such as ollama, lmstudio, llama-server, and vLLM; samples system metrics at a fixed interval $S_{mig}$11 s; invokes inference calls against Ollama’s REST API and OpenAI-compatible endpoints; and records token-level metadata. The backend aggregates raw samples into per-inference summaries and pushes results to SQLite. The dashboard is implemented with Next.js and React and reads the SQLite database through a lightweight Node.js API layer (Allen, 12 Dec 2025).

Metric definitions are explicit. Total energy per run is

$S_{mig}$12

in watt-hours when $S_{mig}$13 is in watts and $S_{mig}$14 in hours. Inference latency is

$S_{mig}$15

throughput is

$S_{mig}$16

energy per token is

$S_{mig}$17

latency per token is

$S_{mig}$18

and average power draw is

$S_{mig}$19

When direct power information is unavailable, the toolkit uses the fallback estimate

$S_{mig}$20

The cross-platform benchmarking protocol requires identical prompts, or a common prompt hash, across Ollama, LM Studio, vLLM, text-generation-webui, and related endpoints. Results are aggregated per $S_{mig}$21 for apples-to-apples comparison. The persistence layer uses SQLite tables for models, prompts, benchmark_results, and optional raw_samples, including fields such as energy_wh, duration_s, tokens, wh_per_token, tok_per_s, and quality_score (Allen, 12 Dec 2025).

A personalized recommendation module scores candidate models under user constraints $S_{mig}$22 and $S_{mig}$23. With normalized quantities

$S_{mig}$24

the weighted score is

$S_{mig}$25

with default $S_{mig}$26, $S_{mig}$27, and $S_{mig}$28. The toolkit also implements LLM-as-judge evaluation: a local judge can score accuracy, completeness, clarity, and relevance from $S_{mig}$29 to $S_{mig}$30, and the quality score is

$S_{mig}$31

If no judge is available, a heuristic uses vocabulary diversity, target-length agreement, and structure detection (Allen, 12 Dec 2025).

Experimental excerpts illustrate the targeted trade-off analysis. Summarization tasks with $S_{mig}$32–$S_{mig}$33 tokens consumed $S_{mig}$34–$S_{mig}$35 Wh, whereas long-form tasks with $S_{mig}$36–$S_{mig}$37 tokens consumed $S_{mig}$38–$S_{mig}$39 Wh. Energy per token for gemma-3-1b was $S_{mig}$40–$S_{mig}$41 Wh/token, versus $S_{mig}$42–$S_{mig}$43 for gemma-3n-e4b. On the prompt “Explain quantum computing” with gemma-3-1b (Q4), Ollama yielded $S_{mig}$44 Wh, $S_{mig}$45 s, $S_{mig}$46 tok/s, $S_{mig}$47 Wh, $S_{mig}$48, whereas LM Studio yielded $S_{mig}$49 Wh, $S_{mig}$50 s, $S_{mig}$51 tok/s, $S_{mig}$52 Wh, $S_{mig}$53 (Allen, 12 Dec 2025). Averaged across $S_{mig}$54 tasks on LM Studio (Q4), gemma-3-1b achieved $S_{mig}$55 Wh, $S_{mig}$56 tok/s, $S_{mig}$57 Wh/token, average $S_{mig}$58, while gemma-3n-e4b consumed $S_{mig}$59 Wh, delivered $S_{mig}$60 tok/s, used $S_{mig}$61 Wh/token, and also averaged $S_{mig}$62. The paper’s stated insight is that model choice outweighs platform choice for efficiency, and that small, quantized models often suffice for well-defined tasks (Allen, 12 Dec 2025).

5. Environmental attitudes, steerability, and governance

A separate EnviroLLM strand concerns the evaluation of environmental attitudes embedded in LLM outputs (Kunkel et al., 1 Jun 2026). The benchmark in "Greener Than Humans? Environmental Attitudes in LLMs" draws $S_{mig}$63 cognition items and $S_{mig}$64 affect items verbatim from the German Federal Environment Agency’s “Umweltbewusstsein in Deutschland” survey, reframes $S_{mig}$65 behavioural items as recommendation queries, augments them with $S_{mig}$66 quantitative CO$S_{mig}$67-reduction queries, and adds $S_{mig}$68 willingness-to-pay questions. The evaluation covers $S_{mig}$69 LLMs, $S_{mig}$70 proprietary and $S_{mig}$71 open-weight, under zero-temperature API or local inference (Kunkel et al., 1 Jun 2026).

The cognition and affect indices are normalized to a $S_{mig}$72–$S_{mig}$73 scale: $S_{mig}$74 CO$S_{mig}$75-reduction potential is defined as

$S_{mig}$76

The benchmark also defines a willingness-to-pay index normalized against reference social costs (Kunkel et al., 1 Jun 2026).

The reported comparison with German human benchmarks is specific. For affect, UBS$S_{mig}$77 has mean $S_{mig}$78 and SD $S_{mig}$79, whereas the LLM mean is $S_{mig}$80 with $S_{mig}$81 CI $S_{mig}$82, Cohen’s $S_{mig}$83. For cognition, the UBS mean is $S_{mig}$84 with SD $S_{mig}$85, whereas the LLM mean is $S_{mig}$86 with $S_{mig}$87 CI $S_{mig}$88, again with $S_{mig}$89. LLM-recommended $S_{mig}$90 averages $S_{mig}$91 t/year with range $S_{mig}$92–$S_{mig}$93, compared with human-population typical behaviour of approximately $S_{mig}$94 t/year. Most models place willingness to pay for CO$S_{mig}$95 below the social-cost reference of $S_{mig}$96/t, clustering between $S_{mig}$97 and $S_{mig}$98 €/t. The study reports no detectable correlation between scores and model size, country of origin, or release year, with Pearson $S_{mig}$99 (Kunkel et al., 1 Jun 2026).

Robustness and steerability are central findings. Variations in language, household context, answer-option order, temperature from $\pi$00 to $\pi$01, and API versus self-hosted execution produce variance of at most $\pi$02 in indices. Persona and sycophancy effects are quantified through the shift metric

$\pi$03

Persona Sensitivity $\pi$04, Sycophancy Sensitivity $\pi$05, UBS Persona Accuracy $\pi$06, and $\pi$07nd-versus-$\pi$08st-person Distance $\pi$09 are then defined as averages or norms over role-specific shifts (Kunkel et al., 1 Jun 2026). The paper reports that Grok-4.1 is most steerable with $\pi$10, whereas GPT-5.2 is least with $\pi$11. The governance response proposed in the paper includes publication of question banks, prompts, and raw outputs; logging of model versions and API settings; human-in-the-loop review; regular re-benchmarking; thresholds for persona sensitivity and sycophancy; and human review when $\pi$12st-person and $\pi$13nd-person answers diverge beyond a predefined $\pi$14 (Kunkel et al., 1 Jun 2026).

This line of work situates EnviroLLM not as a generator of environmental predictions or classifications, but as an object of normative measurement. A plausible implication is that environmental specialization cannot be evaluated solely by task accuracy; it may also require explicit tests of robustness, steerability, and governance.

6. Environmental-domain ESG text classification

In ESG text classification, an EnviroLLM appears as EnvLlama 2-Qlora, a domain-specific fine-tuned model for identifying environmental content within textual disclosures (Chung et al., 2024). The environmental dataset contains $\pi$15 expert-annotated text snippets drawn from a corpus of over $\pi$16 million documents, including annual reports, sustainability disclosures, and news articles. Each snippet is binarily labeled “Environmental” or “Not Environmental.” For LLM fine-tuning, the texts are stratified into a training set of $\pi$17 positive and $\pi$18 negative examples, a test set of $\pi$19 positive and $\pi$20 negative examples, and a held-out evaluation subset of the remaining $\pi$21 samples, balanced by repetition sampling (Chung et al., 2024).

The base model is LLaMA 2-7B-hf without RLHF. The stated architecture is a decoder-only Transformer with $\pi$22 stacked self-attention layers, $\pi$23 attention heads per layer, hidden dimension $\pi$24, feed-forward inner dimension $\pi$25, and vocabulary size $\pi$26 tokens using a SentencePiece/BPE tokenizer. Fine-tuning uses QLoRA with the base model loaded in NF4 format with nested quantization. Low-rank adapters are inserted into each self-attention and feed-forward projection, with rank $\pi$27, LoRA $\pi$28, and dropout $\pi$29; all original LLaMA 2 weights are frozen and only adapter weights are updated (Chung et al., 2024).

The listed hyperparameters are learning rate $\pi$30, weight decay $\pi$31, batch size per GPU $\pi$32, gradient accumulation steps $\pi$33, number of epochs $\pi$34, optimizer paged_adamw_32bit, cosine decay with $\pi$35 warm-up, max gradient norm $\pi$36, fp16 training, and maximum sequence length $\pi$37 tokens. The training objective is binary cross-entropy with regularization: $\pi$38

$\pi$39

Performance is reported on the held-out test set of $\pi$40 samples. For EnvLlama 2-Qlora, Accuracy $\pi$41, Precision $\pi$42, Recall $\pi$43, and $\pi$44. The comparison baseline values are FinBERT-ESG at $\pi$45 for Accuracy, $\pi$46 Precision, $\pi$47 Recall, and $\pi$48 $\pi$49; SVM and XGBoost both at $\pi$50 across Accuracy, Precision, Recall, and $\pi$51 except FinBERT-ESG precision (Chung et al., 2024). The reported improvement is $\pi$52 points in accuracy and $\pi$53 over FinBERT-ESG and classical baselines. This is a narrower usage of EnviroLLM than the multimodal or agentic variants: the model is specialized to environmental semantics within corporate and financial text rather than to environmental processes or environmental decision support more broadly.

7. Cross-cutting patterns, limitations, and future directions

Across these papers, several recurrent design principles are explicit. First, EnviroLLM systems externalize context that conventional LLM prompting would often leave implicit. In (Li et al., 10 Feb 2026), the external context is the executable environment with logs and diagnostics. In (Li et al., 2024), it is a multimodal environmental state including text linearizations and trend images. In (Allen, 12 Dec 2025), it is the resource trace of local inference. In (Kunkel et al., 1 Jun 2026), it is a benchmarked normative frame built from survey instruments and persona prompts. In (Chung et al., 2024), it is domain-specific supervision from expert-annotated environmental disclosures.

Second, these systems tend to make evaluation operational and quantitative. Code migration uses compile success, runtime success, test-pass rate, dependency drift, and setup time (Li et al., 10 Feb 2026). Ecosystem modeling uses RMSE and MAE, with explicit ablations for imputation and multi-granularity (Li et al., 2024). Local-AI benchmarking uses energy, latency, throughput, and quality-efficiency trade-offs (Allen, 12 Dec 2025). Environmental-attitude evaluation introduces normalized indices, role-shift distances, and governance thresholds (Kunkel et al., 1 Jun 2026). ESG classification uses standard accuracy, precision, recall, and $\pi$54 (Chung et al., 2024).

Third, the literature indicates different forms of incompleteness and instability. In LITE, the challenge is missing sensors and distribution shift (Li et al., 2024). In environment-in-the-loop migration, it is incomplete dependency manifests, decoupled workflows, and agent oscillation (Li et al., 10 Feb 2026). In environmental-attitude benchmarking, it is contextual sensitivity and sycophancy (Kunkel et al., 1 Jun 2026). In local-AI optimization, it is the variability of energy and quality across models, platforms, and quantization levels (Allen, 12 Dec 2025). This suggests that EnviroLLM research is organized around the problem of making latent environment-dependent failure modes observable and optimizable.

The future directions recorded in the literature remain correspondingly diverse. For environment-in-the-loop migration, proposed directions include reinforcement learning for agent coordination, learning environment semantics from CI logs and build scripts, integration with container orchestration such as Kubernetes operators driven by LLMs, extension to multi-repo and microservices migrations, and fine-grained security- and performance-oriented environment tuning (Li et al., 10 Feb 2026). For the broader sustainability-governance strand, the explicit priorities are transparency, traceability, re-benchmarking, adjudication layers, and restrictions on unmonitored use in high-stakes contexts (Kunkel et al., 1 Jun 2026).

Taken together, the literature suggests that EnviroLLM is presently a family resemblance term rather than a settled category. Its members differ substantially in architecture and objective, but they share a methodological commitment: the “environment” is not treated as background metadata but as a modeled variable, a control target, or an evaluation axis.