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Seed2.0 Model Card: Towards Intelligence Frontier for Real-World Complexity

Bytedance Seed

arXiv:2607.00248v1cs.AI

TL;DR

Existing evaluations do not fully capture complex, long-horizon real-world tasks requiring reliable instruction execution and multimodal understanding. Seed2.0 introduces a needs-driven evaluation framework and targets long-tail knowledge and complex instruction following, demonstrating initial ability on complex real-world tasks with strong reasoning, visual, and search capabilities.

  • Problem

    Existing evaluation and modeling efforts provide limited coverage of complex, multi-step real-world tasks requiring precise instruction execution and long-horizon performance.

  • Method

    Seed2.0 builds a needs-driven evaluation framework spanning Science Discovery, Vibe Coding, Context Learning, and Real-World Tasks to guide model development.

  • Results

    Seed2.0 demonstrates initial capability on complex real-world tasks, with world-leading reasoning, visual understanding, and search capabilities; Seed2.0 Pro scores 0.781 on τ 2-Bench.

  • Takeaways & Limitations

    Seed2.0 begins to handle complex, long-range real-world tasks and deliver greater value across applications serving hundreds of millions of users.

  • Takeaways & Limitations

    Complex API orchestration and long-horizon code execution remain unresolved limitations for Seed2.0 Pro.

Abstract

from arXiv · show

We present Seed2.0, a model series that takes a meaningful step toward solving complex, real-world tasks. Our approach begins with identifying users' genuine needs and constructing a reliable, forward-looking evaluation system by selecting and abstracting benchmarks grounded in these needs and in realistic, complex scenarios. Guided by this evaluation system, Seed2.0 targets two persistent challenges, long-tail knowledge and complex instruction following, substantially improving the model's reliability on intricate, long-horizon tasks. Beyond these, Seed2.0 delivers world-leading reasoning intelligence, visual understanding, and search capabilities that address the most common needs of a broad user base. Through extensive real-world use cases documented in this model card, we demonstrate that Seed2.0 begins to exhibit the ability to handle initial complex real-world tasks, delivering greater value to hundreds of millions of users.

1 Introduction

Seed2.0 targets real-world complexity by prioritizing visual understanding, fast inference, reliable complex instruction execution, and coding assistance. It introduces a four-dimensional evaluation framework for tracking progress on complex, long-horizon tasks while acknowledging remaining gaps with international frontier models.

  • User-Centered Design: Seed2.0 prioritizes visual and multimodal queries, inference latency, reliable complex instruction execution, and seamless coding assistance under large-scale online deployment.These priorities are motivated by their direct effects on interactive user experience.
  • User-Centered Design: Seed2.0 strengthens visual reasoning with reduced hallucination [28] [44] [127] and improves structured extraction from documents and figures [75, 110].The focus reflects the substantial share of real-world queries involving screenshots, charts, scanned documents, and mixed-media content.
  • User-Centered Design: Seed2.0 provides Pro, Lite, and Mini models so developers can balance performance and inference speed for their use cases.The design treats latency as a direct determinant of user experience.
  • User-Centered Design: Seed2.0 treats reliable execution of complex, multi-step instructions as a first-class requirement, addressing structured reasoning and constraint satisfaction beyond factual recall.Benchmarks such as DeR2 [125] and CL-bench [34] capture this demand.
  • Motivation and Scope: Despite strong performance on competition-level problems, current agents often fail at practical end-to-end tasks because real-world work spans long horizons and requires effective autonomous workflows [5] [47] [70].Seed2.0 therefore extends its ambition toward research-level reasoning, including Erdős problems and Scientific Coding [4] [80] [86].
  • Real-World Complexity: Seed2.0 establishes a four-dimensional framework—Science Discovery, Vibe Coding, Context Learning, and Real-World Tasks—to benchmark and guide progress on complex, long-horizon agent performance.The framework is intended both as a benchmark suite and as an iterative development guide.

2 Seed2.0 Deployment Patterns and Developer Behavior

Seed2.0 deployment in mainland China concentrates in enterprise-facing digital industries and cognitively intensive workflows requiring heterogeneous-data processing, long contexts, structured generation, and tool-augmented execution. Agentic coding usage is dominated by frontend development and maintenance, while tiered pricing supports both complex and high-throughput enterprise workloads.

  • Deployment patterns: Enterprise MaaS usage concentrates in Internet and other information-dense industries, while unstructured information processing, education, content creation, and search or recommendation dominate deployment scenarios.Traditional verticals including manufacturing, automotive, and communication each account for less than 1% of usage; specialized applications remain at earlier deployment stages.
  • Developer behavior: Frontend development substantially dominates agentic coding requests, with page layout, styling, and UI logic queries far exceeding backend, client-side, and full-stack alternatives.The distribution is attributed in part to frontend work’s iterative visual-feedback loops, which encourage frequent model interactions.
  • Cost and model tiers: Seed2.0 token pricing is roughly an order of magnitude lower than frontier models while delivering comparable user experience, enabling high-volume enterprise workflows that costlier alternatives may make infeasible.Table 1 compares API token prefill and decode prices across major foundation models; the claim concerns production reasoning and generation quality rather than a specific listed price.
  • Deployment patterns: Seed Model functions as a workflow-oriented MaaS foundation, combining multimodal understanding, long-context reasoning, structured generation, and tool-augmented execution for enterprise task completion.Dominant workflows require processing heterogeneous data at scale, synthesizing cross-domain knowledge, following multi-step instructions, and producing high-fidelity structured outputs.
  • Developer behavior: Frontend languages collectively dominate code touched, Vue.js leads React by more than three times, and bug fixing is the most common task followed by refactoring and documentation.These patterns indicate strong demand for frontend understanding and debugging capabilities, including tracing errors, stack traces, and program state.
  • Cost and model tiers: Seed2.0 Pro targets complex reasoning and long-context tasks, Lite balances general-purpose capability and cost, and Mini offers decode pricing under $0.50 per million tokens for high-throughput applications.The tiers are designed for differing workload requirements, including latency-sensitive use cases where minimizing cost per query is essential.

3 Comprehensive Evaluation Framework and Methodology

The evaluation framework measures Seed2.0 across fundamental reasoning, instruction following, broad knowledge, vision, and realistic long-horizon tasks using frontier-model comparisons and benchmarks grounded in professional and agentic workflows. It emphasizes reliable assessment of long-tail knowledge, multimodal capability, scientific discovery, software repository completion, context learning, and end-to-end task fulfillment.

  • Fundamental Capabilities: Seed2.0 is evaluated against representative frontier models across reasoning, complex instruction following, broad knowledge understanding, and an extensive benchmark suite.The suite includes academic, coding, knowledge, instruction-following, and internal benchmarks designed to reflect high-value capabilities.
  • Knowledge Evaluation: LPFQA, Encyclo-K, and HLE-Verified target reliable evaluation of long-tail professional and expert knowledge through realistic questions, compositional book-derived tests, and expert-verified problems.LPFQA covers domains including programming, finance, engineering, medicine, and applied science; Encyclo-K supports zero-shot and few-shot evaluation; HLE-Verified filters unclear or unverifiable questions.
  • Vision Evaluation: Vision evaluation spans 50 public image benchmarks and 24 public video benchmarks across categories including multimodal mathematics, STEM, visual puzzles, perception, VQA, spatial understanding, documents, charts, and long-context understanding.The methodology uses category-specific benchmark suites and metrics, including aggregated MMMU-Pro scores, normalized HiPhO scores, and worst-case DynaMath accuracy.
  • Scientific Discovery: The framework introduces Ainstain Bench and BABE to assess scientific coding and multimodal biological reasoning grounded in interleaved textual and visual evidence.These benchmarks evaluate research-oriented capabilities relevant to scientific workflows and research-style inference.
  • Vibe Coding: NL2Repo-Bench measures end-to-end repository construction from natural-language specifications, including long-horizon execution, cross-file consistency, and dependency management.The benchmark reflects emerging extreme “vibe coding” scenarios.
  • Context Learning and Real-World Tasks: The suite evaluates agentic real-world capability through noisy-document context learning, enterprise and developer scenarios, rubric-based task fulfillment, comparable expert tasks, and executable multi-step planning.DeR2 measures extracting and using information from long technical documents, while GDPVal-Verified, XpertBench, and WorldTravel [107] assess reliable end-to-end completion and goal decomposition.

4 Results

Seed2.0 Pro delivers leading performance across language, reasoning, visual-language, and video understanding tasks, with particular strengths in long-context processing, mathematics, perception, spatial reasoning, and video reasoning. Seed2.0 Lite and Mini provide competitive efficiency-focused alternatives for latency- and cost-constrained deployments.

  • Language, Knowledge, and STEM: Seed2.0 Pro sits within the international leading group across core language capabilities, reflecting optimization for instruction following, long-tail knowledge, long-context stability, code reasoning, and front-end generation.It leads on HealthBench, remains competitive with GPT-5.2 and Gemini-3-Pro on SuperGPQA and Encyclo-K, and matches or edges out Gemini-3-Pro on multiple STEM benchmarks.
  • Mathematics, Code, and STEM Reasoning: Seed2.0 Pro demonstrates advanced mathematical, coding, formal theorem-proving, and STEM reasoning, including a Codeforces Elo of 3020 and gold-medal-level Olympiad mathematics performance.It performs competitively on AIME, HMMT, IMOAnswerBench, MathApex, Putnam-200, and FrontierSci-research, while Seed2.0 Pro matches or edges out Gemini-3-Pro in multiple STEM cases.
  • Instruction Following: 75.26% on Table 7 represents a +2.37% absolute gain over Seed1.8, with the largest improvements in tone control (+15.16%), phrasing adherence (+10.31%), and few-shot learning (+9.53%).These gains improve subtle Chinese pragmatic expression and reliability on multi-constraint prompts.
  • Lite and Mini Variants: Seed2.0 Lite and Mini offer strong efficiency–quality trade-offs, with Lite excelling in math and reasoning while maintaining solid instruction-following performance for latency- and cost-constrained deployments.The smaller models remain competitive with OpenAI and Google counterparts across broad benchmark ranges, while Mini also performs strongly on video tasks.
  • Visual-Language Understanding: Seed2.0 Pro achieves state-of-the-art results across visual-language tasks, including mathematics, perception, spatial reasoning, document understanding, and long-context understanding.It scores 88.8 on MathVision, 92.3 on DA-2K, 79.5 on BLINK, 72.4 on DUDE, and 74.8 on MMLongBench, while leading multiple visual puzzle and VQA benchmarks.
  • Video Understanding: Seed2.0 pushes the video-understanding frontier with state-of-the-art results in motion perception, reasoning, streaming, and long-video understanding, including 89.5 on VideoMME.It surpasses human performance on VideoReasonBench, reaches 37.4% accuracy on Morse-500, and equips the entire model family with VideoCut tool use for long-video reasoning.

5 Use Cases of Seed2.0

Across repository construction, software maintenance, GUI control, code generation, competitive problem solving, and scientific debugging, Seed2.0 handles complex real-world tasks through structured analysis, iterative correction, and rigorous validation. The case studies show reliable execution under ambiguity, partial failure, and specialized technical constraints.

  • Project repository construction: Seed2.0 constructs and validates complete repositories from requirements, completing NL2Repo in 37 interaction rounds and passing all 22 tests with installation and cross-directory import checks.Its workflow proceeds from requirements through implementation, debugging, testing, documentation, and packaging verification.
  • Software maintenance and refactoring: In software refactoring, Seed2.0 delivers production-level maintainability through phased validation, with 56 passing unit tests, verified import compatibility, accurate handler outputs, and complete reference coverage.The work spans OS compatibility, logging, CLI/API consistency, concurrency safety, and user-facing output correctness, including end-to-end propagation of an --all-commits flag.
  • Competitive problem solving: Seed2.0 Pro achieves a Pass@8 score of 73.02%, significantly outperforming GPT-5.2 and Gemini-3-Pro, while earning Gold Medals in all five contests against human teams.The results are reported in Figure 7.
  • GUI understanding and control: Seed2.0 performs semantic GUI tasks by tracking state, recovering from tool and dialog errors with micro-plans, and re-anchoring actions to canonical workflow invariants.The demonstrated tasks include CAD operations and audio editing, with reflective self-correction after missed clicks, incorrect selections, and misaligned placement.
  • Real-world code generation: Seed2.0 solves application-oriented coding tasks by implementing specified interactive website features and generating a 3D phase space from projected input.The website recreation includes animations, 3D card effects, and dynamic progress-bar textures, while the phase-space example demonstrates code generation for a scientific visualization.
  • Scientific and cryptanalytic reasoning: On specialized scientific software, Seed2.0 diagnoses and fixes a Qiskit Solovay–Kitaev bug by tracing the SU(2)/SO(3) representation mismatch, verifying a pure π phase sign flip, and correcting post-processing rather than the recursion.The case requires reproducing the failure, localizing phase assignment in solovay_kitaev.py, and applying mathematically grounded debugging; related cryptanalytic work also used repository analysis, key validation against 32 pairs, and decryption of 100 ciphertexts.

6 Conclusion

Seed2.0 advances complex real-world task solving by grounding evaluation in users’ needs and realistic scenarios, targeting long-tail knowledge and complex instruction following. It also demonstrates strong reasoning, visual understanding, search, and early capability on complex long-horizon tasks.

  • 6 Conclusion: Seed2.0 builds a reliable, forward-looking evaluation system from benchmarks selected or abstracted around users’ needs and complex real-world scenarios.The model series specifically targets long-tail knowledge and complex instruction-following problems.
  • 6 Conclusion: 31.4 turns and 7.3 tool calls characterize high-score τ 2-Bench cases, versus 55.0 turns (+75.2%) and 9.6 calls (+31.5%) for low-score cases.High-score Seed2.0 Pro cases dynamically skip redundant device checks, resolve issues within a controlled tool-call budget, and follow single-step JSON constraints; low-score cases enumerate mechanically and over-consume turns.
  • 6 Conclusion: Seed2.0 delivers world-leading reasoning intelligence, visual understanding, and search capabilities addressing common needs for large numbers of users.The model card presents these capabilities as serving hundreds of millions of users.
  • 6 Conclusion: Seed2.0 begins handling complex long-range real-world tasks, as demonstrated by the model card’s extensive real-use-case evidence.The conclusion characterizes this as an initial ability that brings greater value to hundreds of millions of users.

7 Contributors · A FreeCAD Parametric Modeling: GUI Agent Case Study

The section identifies the contributors to the work, listing them alphabetically by first name and noting that some names are internal company aliases. The supplied passages cover the contributor roster only; no FreeCAD case-study content is provided.

  • 7 Contributors: Contributors are listed in alphabetical order by their first names.The section explicitly states that the ordering is alphabetical by first name.
  • 7 Contributors: Some listed names are internal aliases used by the company.This qualification accompanies the contributor-listing note.
  • 7 Contributors: The roster begins with contributors whose first names range from Allan through Chen.The opening roster passage lists names beginning with Allan and continuing through Chen.
  • 7 Contributors: The middle roster continues through contributors whose first names include Cheng, Fan, Feng, and Jian.Successive passages extend the alphabetical list through these first-name ranges.
  • 7 Contributors: Later roster entries include first names spanning Jing, Kai, Liang, Peng, and Tian.The contributor passages continue alphabetically across these ranges.
  • 7 Contributors: The roster concludes with contributors whose first names span Yuan, Zhi, Zhong, and Zuquan.The final passages list the closing alphabetical ranges and end with Zuquan Song.

A.1 Task Overview … Phase 3: Implementing the F-function Inverse

The case studies demonstrate reliable execution of a 96-step FreeCAD modeling workflow with programmatic verification and an FEAL cryptanalytic workflow whose F-function inverse passed multiple test vectors. Together, they emphasize adaptive error recovery, structured interaction strategies, and exploiting cipher structure to reduce attack complexity.

  • A.1 Task Overview: The FreeCAD task required creating a parametric solid in FreeCAD 1.0.2 on Ubuntu and calculating its final volume and surface area.The workflow included a cylindrical base, a rectangular top boss, and Python-scripted verification.
  • A.2 Complete Workflow Visualization: The 96-step modeling workflow progressed through nine phases from software configuration and sketching to extrusion, scripting, and final verification.Figure 19 identifies the critical milestones, while the agent successfully navigated interface challenges and recovered from errors.
  • Phase 4: First Pad Extrusion (Steps 40-52): The completed CAD workflow used constrained sketches and two pad extrusions to create the cylindrical base and rectangular boss, completing the compound solid model.The base used an 80mm diameter and 40mm height, while the second pad used a 20mm length after defining the rectangular sketch.
  • Phase 7: Programmatic Verification (Steps 83-96): Volume = 231061.93 mm³ and Surface Area = 23306.19 mm² were obtained by Python verification of the completed FreeCAD solid.The model comprises an 80mm-diameter, 40mm-high cylindrical base and a 50mm × 30mm × 20mm rectangular boss.
  • A.3 Detailed Execution Trace: The agent recovered from eight failed tool selections by switching from ambiguous toolbar icons to menu navigation and from unreliable direct clicks to the Elements panel.These adaptations supported circle selection, dimensional constraints, pad creation, and other critical modeling operations.
  • A.6 Lessons for GUI Agent Design: The GUI case study identifies icon ambiguity, unreliable geometric selection, and the need for hybrid fallback strategies as design considerations for automation systems.OCR-based tooltip reading, improved icon classification, hierarchical element trees, and menu-based navigation are proposed responses.
  • Phase 3: Implementing the F-function Inverse: F^-1(F(x)) = x passed for multiple test vectors, validating the implemented inverse F-function for the FEAL meet-in-the-middle attack.The inverse reverses the rotation-based G-transformations using right rotations, modular subtraction, XOR reconstruction, and byte recombination.

Phase 4: Key Recovery Implementation

The key-recovery attack precomputes reverse and delta maps for candidate k0 values, then matches backward from ciphertext candidates k3 to derive and verify the remaining keys. This process produces candidate keys after consistency checks across multiple pairs.

  • Step A: Build k0 Delta Map: The attack builds a reverse lookup for the non-injective expand() function by enumerating 2^20 candidates, yielding 1,047,882 entries with collisions.Each entry maps an expanded value to a candidate k.
  • Step A: Build k0 Delta Map: The k0 delta map stores ∆ = A0 ⊕ A1 7→ (k0, A0, A1) and contains 933,294 unique deltas.The map is constructed from intermediate values computed for pairs 0 and 1.
  • Step B: Match with k3: The attack iterates k3 ∈[0, 220) and computes backward from the ciphertext to search for matches.A match yields potential K2 = A0 ⊕ B0, which is checked against exp_map before deriving k1.
  • Step B: Match with k3: Candidate keys are found only after verifying K2 through exp_map, deriving k1 from another pair, and checking K1 consistency with pair 3.These checks provide the final cross-pair consistency validation.

Phase 5: Verification and Decryption

The recovered keys were verified against all 32 plaintext–ciphertext pairs and independently cross-checked with the provided decryption binary. Reversing the four Feistel rounds then decrypted 100 target ciphertexts successfully.

  • Key Verification Against All Pairs: All 32 plaintext–ciphertext pairs verified successfully, confirming that the recovered keys are correct.
  • Key Verification Against All Pairs: The provided decrypt binary reproduced the expected plaintext 367472469947502207 from ciphertext 14898973196205001976 using the recovered keys.
  • Decrypting Target Ciphertexts: Decryption reversed the final swap and processed the four Feistel rounds backward with round keys in reverse order.
  • Decrypting Target Ciphertexts: 100 ciphertext values were decrypted successfully and saved to plaintexts.txt.

B.3 Attack Performance Analysis · B.4 Key Findings and Agent Behaviors

The agent replaced infeasible brute-force cryptanalysis with a meet-in-the-middle attack, achieving a reported ∼1018-times speedup. It also derived the inverse F-function, systematically verified the recovered key, and implemented efficient modular code and precomputation.

  • B.3 Attack Performance Analysis: 2 × 220 ≈2.1 × 106 operations versus 280 ≈1.2 × 1024 operations, yielding a reported ∼1018-times speedup for the meet-in-the-middle attack.The brute-force approach was described as infeasible, while the meet-in-the-middle strategy exploited the Feistel structure.
  • B.4 Key Findings and Agent Behaviors: The agent identified direct brute force as infeasible and autonomously designed a meet-in-the-middle strategy reducing complexity to O(221) through the Feistel structure.This demonstrates cryptanalytic reasoning tailored to the cipher’s structure.
  • B.4 Key Findings and Agent Behaviors: The agent derived F −1 by analyzing the composition of G-functions, rotation operations, modular arithmetic, and bit-rotation properties.The inversion required understanding how these operations compose within the F-function.
  • B.4 Key Findings and Agent Behaviors: The agent built a delta map from pairs 0 and 1, derived k1 from pair 2, checked consistency with pair 3, and verified it against all 32 known pairs.It also cross-checked the result against the original decrypt binary.
  • B.4 Key Findings and Agent Behaviors: The agent used reverse lookup tables for the expand map and delta map to avoid redundant calculations during the search phase.This demonstrated optimization awareness during the attack implementation.
  • B.4 Key Findings and Agent Behaviors: The implementation was clean and modular, using helper functions including getleft, getright, merge, g_function, f_function, and f_inv_function.The listed helpers covered block manipulation, the G and F functions, and inverse computation.

B.5 Recovered Keys and Final Results · B.6 Lessons for Cryptanalysis Agent Design · C NL2Repo: Python-Decouple Library Implementation

The case study recovered and verified all round keys, decrypted the ciphertext corpus, saved the plaintexts, and cross-validated results with the original binary. It also highlights algorithm analysis, mathematical reasoning, and autonomous attack-strategy design as capabilities for cryptanalysis agents.

  • B.5 Recovered Keys and Final Results: Recovered all 4 round keys and verified them against 32 pairs with 100% accuracy, successfully decrypting 100 ciphertexts.The recovered 20-bit round key seeds are reported in Table 18.
  • B.5 Recovered Keys and Final Results: Saved the decrypted plaintexts to plaintexts.txt and cross-validated the results using the original decrypt binary.
  • B.6 Lessons for Cryptanalysis Agent Design: Together, these capabilities are presented as essential components of autonomous security research agents.
  • B.6 Lessons for Cryptanalysis Agent Design: Algorithm analysis involves reading cryptographic implementations, identifying structural weaknesses in Feistel networks, and recognizing reduced keyspaces.
  • B.6 Lessons for Cryptanalysis Agent Design: Mathematical reasoning requires deriving inverse functions through algebraic manipulation and understanding composition properties to work backward from ciphertexts.
  • B.6 Lessons for Cryptanalysis Agent Design: Attack-strategy design involves autonomously selecting appropriate cryptanalytic techniques, including meet-in-the-middle methods.

C.1 Task Overview · C.2 Agent Execution Trace · C.3 Key Agent Capabilities Demonstrated

The task required implementing a complete, installable Python configuration-management library from a 39KB natural-language specification. The agent translated implicit requirements into compatible code, iteratively tested it, and demonstrated capabilities in specification parsing, debugging, and compatibility preservation.

  • C.1 Task Overview: The benchmark required a production-ready Python library supporting multi-source configuration, type conversion, validation, Docker secrets, packaging, tests, and a specification-matching API.The implementation target was repository-level code generation from a 39KB natural-language specification.
  • C.2 Agent Execution Trace: The agent first extracted exact interfaces, file-structure requirements, parsing rules, and embedded test specifications from the workspace documentation.Code snippets in the specification served as implicit behavioral tests.
  • C.2 Agent Execution Trace: Core implementation covered RepositoryEnv, RepositoryIni, RepositorySecret, Config, AutoConfig, Csv, and Choices across more than 300 lines.The agent prioritized decouple.py as the library’s central module.
  • C.2 Agent Execution Trace: Implementation decisions addressed empty-string booleans, Python 3.12 ConfigParser compatibility, matching-quote stripping, and missing Docker-secrets directories.These choices handled edge cases and compatibility constraints identified during specification analysis.
  • C.2 Agent Execution Trace: The agent configured the package for installation, added documentation and development tooling, and created 22 tests covering the library’s functionality.The project included setup.py, README.rst, repository configuration files, and tests/test_decouple.py.
  • C.2 Agent Execution Trace: Test-driven iteration fixed empty-string boolean conversion and fixture scope errors, after which all 22 tests passed and package installation was verified.The workflow progressed from one failure to four errors and then to a passing test suite.
  • C.3 Key Agent Capabilities Demonstrated: The demonstrated capabilities included structured requirement extraction, error-driven debugging against Python 3.12 documentation, and preserving expected API exports for backwards compatibility.These capabilities connected natural-language requirements to implementation changes and compatibility checks.

C.4 Performance Metrics … D.2 Agent Execution Trace

The implementation produced a fully functional, installable Python package, while the SWE-bench Pro refactoring preserved behavior, compatibility, test coverage, and warning-filtering correctness. The agent coordinated migration across implementation, imports, tests, and verification.

  • C.4 Performance Metrics: The task was completed as a fully functional, installable Python package matching all specifications.
  • D.1 Task Overview: The task required moving QtWarningFilter and hide_qt_warning into qtlog.py while preserving filtering behavior, imports, tests, and warning suppression.
  • D.2 Agent Execution Trace: The agent coordinated changes across qutebrowser modules and tests, identifying three required consistency-preserving edits during repository exploration.
  • D.2 Agent Execution Trace: The migration preserved exact whitespace handling because record.msg.strip().startswith() matches warning patterns despite leading or trailing spaces.
  • D.2 Agent Execution Trace: Backward compatibility was maintained by re-exporting hide_qt_warning and QtWarningFilter from log.py, with both import paths resolving to the same function object.
  • D.2 Agent Execution Trace: The relocated tests covered pass-through behavior, exact and prefix suppression, and whitespace handling, while real usage filtered only the targeted warning.
  • D.2 Agent Execution Trace: The refactoring passed 5 qtlog tests and 51 log tests after relocating implementation and test coverage.

D.3 Refactoring Analysis · D.4 Key Software Engineering Practices

The refactoring used a low-risk, incremental migration process supported by compatibility preservation, dependency analysis, and test-first validation. The documented outcome was zero breaking changes, improved code organization, and all 56 tests passing.

  • D.3 Refactoring Analysis: The code migration is summarized in Table 20.
  • D.4 Key Software Engineering Practices: The agent performed the refactoring in 5 systematic steps rather than using bulk copy-paste, reducing the risk of breaking changes.
  • D.4 Key Software Engineering Practices: A Python re-export pattern, from qtlog import *, preserved the existing API surface while reorganizing internals.
  • D.4 Key Software Engineering Practices: Tests were moved with the code and all tests were validated before success was declared, ensuring no behavior regression.
  • D.4 Key Software Engineering Practices: The agent searched the codebase for all usages, including grep -r "hide_qt_warning", to identify affected modules before changing them.
  • D.4 Key Software Engineering Practices: The migration produced zero breaking changes, improved code organization, and all 56 tests passed.

E Evaluation Details on Advanced Mathematical Reasoning · E.1 Natural Language Proving

Seed2.0 Pro uses an iterative solve–verify–refine pipeline for advanced natural-language mathematical proving. The model generates candidate solutions, detects logical flaws, and refines proofs toward strict Olympiad scoring criteria.

  • E Evaluation Details on Advanced Mathematical Reasoning: The evaluation concerns advanced mathematical reasoning through natural-language proving under rigorous Olympiad-style criteria.
  • E.1 Natural Language Proving: Seed2.0 Pro generates candidate solutions, autonomously identifies logical flaws, and refines its outputs to meet strict Olympiad scoring criteria.This approach is based on the solve-verify-refine framework [52].
  • E.1 Natural Language Proving: Table 2 reports the natural-language proving results for Seed2.0 Pro.
  • E.1 Natural Language Proving: Seed2.0 Pro’s high scores are presented as evidence beyond merely hallucinated correct final answers.
  • E.1 Natural Language Proving: The model demonstrates strong capacity for systematic reasoning in mathematical proof construction.
  • E.1 Natural Language Proving: The iterative pipeline is designed to ensure that each deduction step is logically checked and refined.

E.2 Formal Theorem Proving … E.3.2 Erdos 1051: Irrationality of Infinite Series

Seed2.0 achieves 35.5% Pass@8 on formalized Putnam problems, surpassing comparable general-purpose models and Seed-1.5-Prover. Its case studies also present agent-derived solutions to Erdős problems, including a proof that the specified infinite series is irrational.

  • E.2 Formal Theorem Proving: 35.5% Pass@8 makes Seed2.0 Pro state-of-the-art among comparable general-purpose models, surpassing Gemini-3-Pro and Seed-1.5-Prover on Putnam-200.Putnam-200 contains 200 formalized Putnam problems evaluated in an agent-based multi-turn setup with Lean and Python access.
  • E.3 Case study on Erdos Problems: The Erdős case studies cover challenging open problems in combinatorial geometry and analytic number theory.The problems are Erdős 652 and Erdős 1051.
  • E.3.1 Erdos 652: Distinct Distances in Planar Point Sets: For Erdős 652, the argument constructs an incidence graph from circles centered at selected points and bounds its parallel-edge multiplicity by kt(k + 1).The resulting simple graph has at least kn − k − kt(k + 2) edges.
  • E.3.1 Erdos 652: Distinct Distances in Planar Point Sets: The Erdős 652 proof counts kn − k incidences between points and circles before simplifying the graph for a crossing-number argument.This incidence count combines k(n − k) contributions from non-centers with k(k − 1) from centers.
  • E.3.2 Erdos 1051: Irrationality of Infinite Series: For Erdős 1051, assuming rationality yields a uniform lower bound xm ≥1/Q, while tail and logarithmic recurrences force xm →0.The contradiction relies on bounding normalized logarithmic variables, proving convergence, and obtaining a geometrically decaying tail.
  • E.3.2 Erdos 1051: Irrationality of Infinite Series: The contradiction establishes that the infinite series in Erdős 1051 is irrational.The proof concludes because xm →0 conflicts with the persistent lower bound xm ≥1/Q > 0.
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