Beyond Pretraining: Terminal Coding, Script-Native 3D Modeling, and Financial Document Reasoning

1. Introduction: The Shift from Pretraining to Post-Training

Over the past two years, the competitive landscape for large language models has reached a clear turning point. The pretraining arms race—simply scaling parameters and data—is delivering diminishing marginal returns, while data and compute costs keep climbing. As a result, leading teams have shifted their focus to the post-training stage: instruction tuning, reinforcement-learning-based alignment and decision training, tool use, and multi-step reasoning training. The quality of work at this stage directly determines whether a model can actually get things done, not merely hold a conversation.

Looking at the wave of next-generation models released recently by multiple vendors, the gap between the open-source camp and the closed-source leaders is narrowing significantly, with some tasks even seeing open models pull ahead. More notably, capability leaps are concentrated in three high-value scenarios: terminal-level coding, script-native 3D modeling, and deep reasoning over financial documents. These three threads correspond to R&D efficiency, content production efficiency, and professional analysis efficiency, respectively—and they are what this article unpacks one by one.

2. Terminal-Level Coding: From “Generating Code” to “Completing Tasks”

2.1 The Evaluation Paradigm for Terminal-Level Tasks

Traditional code benchmarks typically give a model some context and ask it to complete a function or produce a static answer, measuring whether the code is written correctly. Terminal-level task benchmarks follow a completely different paradigm: the model is placed in a real terminal work environment and given an engineering task (for example, “fix the failing tests in this repository and submit a patch”). The model must autonomously complete the full loop of reading code, locating the problem, making changes, running tests, reading errors, and revising again, with a verification script scoring the result automatically.

This type of evaluation quantifies three capabilities that were previously hard to measure: long-horizon planning (can the model break a large task into the right sequence of steps), environment feedback comprehension (can it understand real error messages), and error recovery (can it self-correct after failure). It is the most direct yardstick for whether a model can work as an agent. Public evaluation results show that leading open-source models, after systematic post-training, have entered the 60-plus pass-rate range, with the gap to the closed-source top tier narrowed to single digits—the usability threshold has been crossed, and competition is shifting toward stability and cost.

2.2 Cost Is the Second Front

Beyond performance, there is another calculation that must be made. A recommended estimation approach:

Cost per task        = token cost + tool-call overhead + failure retry cost × expected retries
Cost-effectiveness   = task pass rate ÷ cost per task

A common misconception is that a lightweight model is cheaper simply because its per-token price is lower. But if its first-pass success rate is notably low, the tokens and tool-call overhead consumed by repeated retries quickly erase the price advantage, and the cost per solved task ends up higher. A more reasonable engineering approach is tiered scheduling: simple deterministic tasks (format conversion, boilerplate code generation) go to lightweight models, while complex long-horizon tasks (cross-file refactoring, fault repair) go to flagship models.

2.3 Implementation: Sandboxes, Evaluation Loops, and Observation Signals

First, sandbox isolation. Every task should run in a disposable container environment with resource limits, timeout circuit breakers, and snapshot rollback, preventing model-generated commands from contaminating the host environment. Second, build an internal evaluation loop. A terminal task set needs only three elements: a task description, an initial environment image, and an automated verification script. Teams should accumulate tasks from real tickets in their own repositories—this is closer to the business than public leaderboards. Third, feed the outputs of compilation, static checks, and tests back to the model as observation signals, so the model receives structured feedback after every action; this is far more efficient for error correction than returning raw text logs alone.

# Terminal-level task evaluation loop (pseudocode)
for task in task_set:
    sandbox = new_container(image=task.env_image)   # isolated sandbox
    obs = sandbox.exec(task.init_cmd)
    for step in range(MAX_STEPS):
        action = model.plan(task.goal, obs, sandbox.history)
        obs = sandbox.exec(action)                  # real execution, real errors
        if task.verifier.check(sandbox):
            break
    log(score, cost=token_usage + step_cost)

3. Script-Native 3D Modeling: From “Tool Adapter Layers” to “Native Script Generation”

3.1 The Essential Difference Between Two Technical Routes

There have been two main routes for getting models to drive 3D modeling tools. One is the “tool adapter layer” route: atomic operations are individually wrapped for the modeling software, and the model “presses buttons” through a unified calling protocol. The advantages are controllable behavior and easy auditing; the drawbacks are high development and maintenance costs for the intermediate layer, with expressiveness confined to the pre-wrapped set of operations—anything more complex cannot be covered.

The new generation of models has opened up the other route: “script-native generation.” The vast majority of professional modeling software exposes a programmable scripting interface, and the model treats that interface directly as its target programming language, writing in one pass a script that operates across an entire scene—creating geometry, setting materials, adjusting layout, placing lights, and driving animation. The advantages: expressiveness equals the full set of the tool’s native capabilities; scripts are plain text and therefore naturally versionable, reproducible, and parametrically tunable; and the dependency on extra middleware is eliminated, shortening the pipeline and shrinking the failure surface.

3.2 Spatial Reasoning Is the Foundation; Engineering Modeling Has Passed the Practical Threshold

The ceiling of script-native generation depends on the model’s spatial reasoning ability: whether it can mentally perform coordinate transformations, boolean operations, array mirroring, and chamfer selection. Public results on engineering modeling (CAD-type) benchmarks show that leading models with tool assistance are approaching a score of 96, and the open-source camp has reached 90-plus, indicating that this capability has crossed the “usable” threshold and entered the “good” range—sufficient to support everyday creative actions such as asset construction and iterative refinement.

3.3 Productization Loop: Editability Determines How Far It Spreads

In product form, the ideal loop is: natural language or sketch input → script generation → render feedback → iterative correction → online preview and editing → export of a printable file or demo video. What creators really care about is whether the generated asset can be modified, not a one-off render; only editable, reusable assets can enter a production workflow.

Three engineering recommendations: first, inject a summary of the modeling software’s scripting documentation into the model context to reduce hallucinated API calls; second, feed rendered screenshots back to a vision-capable model for self-checking, forming a generate–observe–revise loop; third, bring all generated scripts into a unified asset library under version control, and distill them into reusable components along parametric dimensions.

# Parametric modeling script written directly by the model (pseudocode)
scene = Scene()
base  = scene.add_cylinder(r=0.60, h=0.05, segments=64)
pole  = scene.add_cylinder(r=0.06, h=0.50)
shade = scene.revolve(profile=bezier(P0, P1, P2, P3), angle=360)
shade.set_material(roughness=0.35, transmission=0.60)
scene.add_light(pos=(0, 1.2, 0), power=8, soft_shadow=True)
scene.export_mesh("lamp.stl")        # export a directly 3D-printable file

4. Financial Document Reasoning: Retrieval, Cross-Checking, and Multi-Step Calculation

4.1 Task Profile: Where the Difficulty Lies

The difficulty in financial scenarios is not “question answering” but the authenticity of the input: financial reports and research documents have extremely complex layouts—deeply nested tables, cross-page merges, inconsistent definitions, supplementary footnotes, and scanning noise. The full pipeline tests three capabilities: retrieval (finding the right figures), cross-document verification (aligning definitions), and multi-step numerical reasoning (reconciling the numbers). An error at any link makes the conclusion wrong. Public benchmarks show that leading models have entered the 60-point range on office document QA evaluations, and the deciding factor has shifted from “can it do it” to “is it stable in the details.” This also explains why hallucination testing has become a new key metric: on hard-sample stress tests, the error rate of leading next-generation models has been compressed from above 20% in the previous generation to single-digit percentages—for financial scenarios, this improvement matters more to the business than leaderboard scores.

4.2 A Four-Step Robust Pipeline

Step one, layout parsing and structuring: parse the document into a structure tree of paragraphs, tables, and charts, with tables restored to cell-level precision. Step two, fact extraction: uniformly extract the four elements of line item, amount, period, and definition, and index every number back to its position in the source text. Step three, externalized computation: all numerical reasoning is generated by the model as a calculation script and executed in a sandbox—never let the model do mental arithmetic. This is the most cost-effective way to suppress numerical hallucination. Step four, citation rebinding: every key number in the answer must link back to the source page and cell, with automated validation as a backstop before going live.

# Robust financial document QA pipeline (pseudocode)
doc   = layout_parse(pdf_bytes)                       # layout parsing
facts = extract_figures(doc)                           # extract the four elements
calc  = sandbox_exec(model.write_calc(facts))          # numerical reasoning delegated to code
answer = model.compose(calc, citations=facts)
assert all(c in doc.pages for c in answer.citations)   # citation rebinding check

4.3 Build an Internal Stress-Test Set

Constructing a question bank with mandatory ground-truth answers from your own real business documents, and continuously monitoring hallucination rate and definition-error rate, is essential for financial scenarios. Public leaderboards serve as a horizontal reference, but business safety can only be built on your own test set.

5. Evaluation Methodology: Don’t Just Look at Leaderboards

A three-layer evaluation system is recommended: public benchmarks for horizontal comparison, an internal task set for longitudinal iteration tracking, and gray-release A/B testing to validate real-world gains. Metrics should cover at least first-pass rate, cost per task, end-to-end latency, and hallucination rate. Two cautions: first, beware of benchmark contamination—public question sets may already have leaked into training corpora, so your own task set is the real moat; second, cost decisions should use “cost per solved task,” not per-token pricing.

6. Enterprise Deployment: Reference Architecture and Platform Selection

A reusable reference pipeline is: business entry point → model service layer → tool sandbox (code execution / script rendering) → asset and result storage. Mapped onto general cloud platform capabilities (using the general categories offered by Baidu AI Cloud as an example):

• Container platform: hosts inference services and disposable sandbox tasks, relying on elastic scaling and fault self-healing to handle sudden evaluation and batch generation loads;
• GPU compute: supports both inference and 3D rendering peak loads, with task-granularity scheduling significantly reducing idle cost;
• Object storage: centrally manages the 3D asset library, generated scripts, business documents, and evaluation datasets, with lifecycle policies to control storage overhead;
• Logging and monitoring alerts: continuously tracks token cost, task failure rate, and hallucination rate, with automatic alerting on anomalies, making cost visibility a daily operational routine;
• API gateway and permission system: key management, call auditing, and tenant isolation to safeguard data compliance and security boundaries.

When selecting a platform, check five items one by one: scenario priority (coding / 3D / documents), whether private deployment and data compliance are required, cost-per-task estimates, compatibility coverage of the tooling and scripting ecosystem, and the maturity of the internal evaluation system.

7. Conclusion

Terminal coding, script-native 3D modeling, and financial reasoning may appear to belong to different industries, but at the foundation they converge on the same logic: models are evolving from “text generators” into “actors in an environment,” and the evaluation paradigm is evolving accordingly, from “checking the answer” to “checking the outcome.” For development teams, the most pragmatic order of action is to build the internal evaluation system first, then discuss model selection and scheduling. Evaluation first—that is how you avoid being led by leaderboards through this capability leap, and instead be driven by business results.