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AI4Math Radar

Start with TreeThink: A Modular Tree Search Library for Mathematical Reasoning with LLMs, then Efficient Test-Time Optimization for Multi-Agent Proof Autoformalization. 15 fresh useful item(s) are inside the 21-day content window.

2026-07-18
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America/Los_Angeles Generated 2026-07-18T16:28:10Z JSON data
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13adjacent
73downweighted
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Today's Scan

Start with TreeThink: A Modular Tree Search Library for Mathematical Reasoning with LLMs, then Efficient Test-Time Optimization for Multi-Agent Proof Autoformalization. 15 fresh useful item(s) are inside the 21-day content window.

Source Mix

  • arXiv15

Best Use

Open only the first lane during a busy morning. Older recurring seeds are kept below the daily scan instead of competing with fresh items.

Start Here

The top few items most likely to matter for formal proof agents or Lean-facing AI4Math work.

1
core score 7.9 2026-07-13 arXiv

TreeThink: A Modular Tree Search Library for Mathematical Reasoning with LLMs

Burak S. Akbudak, Zeynel A. Uluşan, Can S. Erer et al.

Tree search algorithms enable systematic exploration of the proof space in neural theorem proving. Existing LLM tree search libraries primarily target natural language reasoning and do not provide native integration with formal verifiers, while theorem prov...

Why it matters Most relevant if you are tracking proof-search loops that use verifier feedback instead of treating Lean as a binary oracle.

Skim cue Skim the search loop: proposal source, verifier call, retry strategy, and stopping rule.

Read if Read if you have 5 minutes and want a direct AI4Math signal.

AI math reasoningLean proof agents
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2
core score 7.9 2026-07-13 arXiv

Efficient Test-Time Optimization for Multi-Agent Proof Autoformalization

Tian-Shuo Liu, Shiyuan Zhang, Zijie Geng et al.

Full-proof autoformalization bridges extensive mathematical proofs in natural language with formally validated reasoning, offering a pathway to elevate the ceiling of verifiable mathematical reasoning. Unlike statement-level formalization, proof autoformali...

Why it matters Most relevant if you are tracking proof-search loops that use verifier feedback instead of treating Lean as a binary oracle.

Skim cue Skim which definitions entered Lean and whether the work adds reusable library surface.

Read if Read if you have 5 minutes and want a direct AI4Math signal.

autoformalizationAI math reasoningverifier feedback
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3
adjacent score 4.9 2026-07-14 arXiv

Graph Puzzles III.1: A Proof of Sabidussi's Compatibility Conjecture

Nikolay Ulyanov

We prove Sabidussi's compatibility conjecture. Let $G$ be a finite connected multigraph in which every vertex has even degree and the minimum degree is at least four, and let $T$ be a closed trail that traverses every edge exactly once. The edges of $G$ can...

Why it matters Direct Lean signal: likely relevant to the formalization or theorem-proving environment around AI4Math agents.

Skim cue Skim which definitions entered Lean and whether the work adds reusable library surface.

Read if Save for later unless the title matches your current proof-agent work.

AI math reasoningLean proof agents
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Worth Opening

Good candidates after the first three. These are plausible paper-tab opens, not a mandatory reading list.

4
adjacent score 4.9 2026-07-12 arXiv

The Prime Digit Distribution Conjecture: A Formal Proof of Average Digit Equidistribution in the Prime Numbers

Mahadee Al Mobin, Md. Shariful Islam

Let $S_n=\{p\in\mathbb{P}:p<10^n\}$, $N_n$ denote the total number of decimal digits occurring in the primes of $S_n$, $C_n(d)$ be the number of occurrences of a digit $d\in\{0,\ldots,9\}$ among those digits, and $P_n(d)$ be the probability of occurrence of...

Why it matters Direct Lean signal: likely relevant to the formalization or theorem-proving environment around AI4Math agents.

Skim cue Skim the abstract first; open the paper only if the method touches proof agents or formal verification.

Read if Save for later unless the title matches your current proof-agent work.

AI math reasoningLean proof agents
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5
adjacent score 3.5 2026-07-16 arXiv

Mech: Mechanised Choreographic Programming

Xueying Qin, Marco Peressotti, Fabrizio Montesi

Choreographic programming (CP) is a programming paradigm for the correct-by-construction development of concurrent and distributed systems: programmers write the intended overall behaviour of a system from a global perspective in a choreography, which is th...

Why it matters Useful for the informal-to-formal bottleneck: it is about preserving mathematical intent across the translation boundary.

Skim cue Skim which definitions entered Lean and whether the work adds reusable library surface.

Read if Save for later unless the title matches your current proof-agent work.

Lean proof agents
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6
adjacent score 3.5 2026-07-16 arXiv

MathCoPilot: An Interactive System for Human-AI Symbiotic Paradigm of Mathematical Research

Junjie Zhang, Jiayu Liu, Wenbin Liu et al.

Existing LLM-based theorem provers have achieved impressive results on formal mathematics benchmarks, yet they remain confined to acting as autonomous agents that prove a stated proposition. In this paper, we propose MathCoPilot, a human-in-the-loop system...

Why it matters Useful for the informal-to-formal bottleneck: it is about preserving mathematical intent across the translation boundary.

Skim cue Skim the task definition, metric, and whether the benchmark has Lean-checkable artifacts.

Read if Save for later unless the title matches your current proof-agent work.

autoformalization
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7
adjacent score 3.5 2026-07-16 arXiv

Exact Computation of Non-Gaussian Mismatch Penalties in Wiener-Hermite Cross-Correlation Identification

Serhii Zabolotnii

Wiener-Hermite cross-correlation identification represents a polynomial response in the Hermite basis. Under Gaussian excitation the basis is orthogonal and a diagonal rule recovers it exactly; under non-Gaussian excitation the same basis is kept, but its G...

Why it matters Direct Lean signal: likely relevant to the formalization or theorem-proving environment around AI4Math agents.

Skim cue Skim the task definition, metric, and whether the benchmark has Lean-checkable artifacts.

Read if Save for later unless the title matches your current proof-agent work.

Lean proof agents
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8
adjacent score 3.5 2026-07-15 arXiv

Ripple: An Open, AI-Formalized Lean 4 Framework for Computing with CRNs

Ho-Lin Chen, Xiang Huang

We present Ripple, an open, AI-formalized Lean 4 framework for the mathematics of computing real numbers with chemical reaction networks (CRNs). Ripple formalizes the full ladder of models -- the GPAC / CRN continuum and the CRN-computable reals, the large-...

Why it matters Infrastructure signal: this may improve premise discovery, dependency retrieval, or library navigation for agents.

Skim cue Skim which definitions entered Lean and whether the work adds reusable library surface.

Read if Save for later unless the title matches your current proof-agent work.

Lean proof agents
Open PDF Semantic Scholar
9
adjacent score 3.5 2026-07-15 arXiv

Generative Compilation: On-the-Fly Compiler Feedback as AI Generates Code

Niels Mündler-Sasahara, Hristo Venev, Dawn Song et al.

Languages with rich static semantics, such as Rust, provide stronger guarantees for AI-generated code, but their strictness makes generation more difficult. Off-the-shelf compilers can provide useful feedback post-generation, but does not guide intermediate...

Why it matters Most relevant if you are tracking proof-search loops that use verifier feedback instead of treating Lean as a binary oracle.

Skim cue Skim the abstract first; open the paper only if the method touches proof agents or formal verification.

Read if Save for later unless the title matches your current proof-agent work.

verifier feedback
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10
adjacent score 3.5 2026-07-15 arXiv

A Fast Quantitative Analyzer for NetKAT

Thomas Lu, Qiancheng Fu, Kevin Batz et al.

When designing a network, engineers must navigate trade-offs (e.g., one topology offers more aggregate bandwidth, another lower latency or better resilience) that demand reasoning about quantitative properties. We present a fast analyzer for quantitative ne...

Why it matters Most relevant if you are tracking proof-search loops that use verifier feedback instead of treating Lean as a binary oracle.

Skim cue Skim which definitions entered Lean and whether the work adds reusable library surface.

Read if Save for later unless the title matches your current proof-agent work.

verifier feedback
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11
adjacent score 3.5 2026-07-14 arXiv

Theory-Level Autoformalization: From Isolated Statements to Unified Formal Knowledge Bases

Marcus J. Min, Mike He, Zhaoyu Li et al.

Autoformalization translates informal natural language into formal, machine-verifiable languages. While most work focuses on individual statements, real formalization efforts are inherently theory-level: they require an entire web of axioms, definitions, an...

Why it matters Useful for the informal-to-formal bottleneck: it is about preserving mathematical intent across the translation boundary.

Skim cue Skim which definitions entered Lean and whether the work adds reusable library surface.

Read if Save for later unless the title matches your current proof-agent work.

autoformalization
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12
adjacent score 3.5 2026-07-14 arXiv

Interchange graphs of (0,1)-matrices are maximally Hamiltonian

Jeffrey S. Baggett, Huiya Yan

For integer vectors R,S let A(R,S) denote the class of (0,1)-matrices with row sum vector R and column sum vector S. Its interchange graph G(R,S) has A(R,S) as its vertex set, two matrices being adjacent when they differ by a single 2 x 2 interchange. Brual...

Why it matters Direct Lean signal: likely relevant to the formalization or theorem-proving environment around AI4Math agents.

Skim cue Skim which definitions entered Lean and whether the work adds reusable library surface.

Read if Save for later unless the title matches your current proof-agent work.

Lean proof agents
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Watch Later

Adjacent formalization or infrastructure signals. Keep them in peripheral vision unless they match an active project.

13
adjacent score 3.5 2026-07-14 arXiv

Faithful Autoformalization of Natural Language Assertions

Hongyi Liu, Madhusudan Parthasarathy, Adithya Murali

Formal contracts are essential for software testing and verification, yet writing them remains labor-intensive and error-prone. LLMs offer a promising path toward autoformalization: synthesizing executable assertions from natural-language specifications and...

Why it matters Useful for the informal-to-formal bottleneck: it is about preserving mathematical intent across the translation boundary.

Skim cue Skim which definitions entered Lean and whether the work adds reusable library surface.

Read if Save for later unless the title matches your current proof-agent work.

autoformalization
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14
adjacent score 3.5 2026-07-14 arXiv

Evidence-Grounded Verified Agentic Reasoning: A Path Toward Eliminating LLM Hallucination in Empirical Inference via Tool-Attested Kernel Proofs

Junyu Ren

Tool access alone does not make LLM empirical reasoning governable: accepted outputs need not descend from attested evidence, and accepted deductions need not hold up under formal scrutiny. We present EG-VAR (Evidence-Grounded Verified Agentic Reasoning), a...

Why it matters Direct Lean signal: likely relevant to the formalization or theorem-proving environment around AI4Math agents.

Skim cue Skim which definitions entered Lean and whether the work adds reusable library surface.

Read if Save for later unless the title matches your current proof-agent work.

Lean proof agents
Open PDF Semantic Scholar
15
adjacent score 3.5 2026-07-14 arXiv

An Agentic Formalization for Certified Quantum Neural Network Design

Mingrui Jing, Lei Zhang, Yusheng Zhao et al.

A central model in quantum machine learning is the quantum neural network (QNN), whose design requires balancing expressivity and trainability. Technically, expressivity is studied through circuit-function analysis, such as quantum signal processing, while...

Why it matters Useful for the informal-to-formal bottleneck: it is about preserving mathematical intent across the translation boundary.

Skim cue Skim which definitions entered Lean and whether the work adds reusable library surface.

Read if Save for later unless the title matches your current proof-agent work.

Lean proof agents
Open PDF Semantic Scholar

Older but Useful

0 relevant item(s) are outside the 21-day content window. Keep them for context, but do not let them drive today's scan.

No older useful items were retained in this run.

Downweighted 73 low-priority match(es), folded for daily reading.
1
negative score 0.8 2026-07-18 GitHub

leanprover/lean4: fix: restore ambient exporting scope in `mkAuxDefinition` (#14442)

Sebastian Ullrich

Recent commit on leanprover/lean4.

Why it matters Toolchain signal: open only if the commit touches proof search, elaboration, tactics, Lake, or mathlib behavior you depend on.

Skim cue Skim only the changed subsystem and whether it affects your Lean workflow.

Read if Open only if you are debugging Lean or mathlib locally.

Open Semantic Scholar
2
negative score 0.8 2026-07-18 GitHub

leanprover/lean4: fix: don't resynthesize unwrapped nested proofs in the grind canonicalizer (#14439)

Leonardo de Moura

Recent commit on leanprover/lean4.

Why it matters Toolchain signal: open only if the commit touches proof search, elaboration, tactics, Lake, or mathlib behavior you depend on.

Skim cue Skim only the changed subsystem and whether it affects your Lean workflow.

Read if Open only if you are debugging Lean or mathlib locally.

Open Semantic Scholar
3
negative score 0.8 2026-07-18 GitHub

leanprover/lean4: fix: avoid expensive `isDefEqD` in `grind` (#14444)

Leonardo de Moura

Recent commit on leanprover/lean4.

Why it matters Toolchain signal: open only if the commit touches proof search, elaboration, tactics, Lake, or mathlib behavior you depend on.

Skim cue Skim only the changed subsystem and whether it affects your Lean workflow.

Read if Open only if you are debugging Lean or mathlib locally.

Open Semantic Scholar
4
negative score 0.8 2026-07-18 GitHub

leanprover-community/mathlib4: feat(RingTheory/Localization/Integer): cardinality of `finsetIntegerMultiple` (#41688)

Thomas Browning

Recent commit on leanprover-community/mathlib4.

Why it matters Infrastructure signal: this may improve premise discovery, dependency retrieval, or library navigation for agents.

Skim cue Skim only the changed subsystem and whether it affects your Lean workflow.

Read if Open only if you are debugging Lean or mathlib locally.

Open Semantic Scholar
6
negative score 0.8 2026-07-18 GitHub

leanprover-community/mathlib4: feat(Analysis/Convex/TotallyBounded): strengthen to an iff (#41226)

Yaël Dillies

Recent commit on leanprover-community/mathlib4.

Why it matters Infrastructure signal: this may improve premise discovery, dependency retrieval, or library navigation for agents.

Skim cue Skim only the changed subsystem and whether it affects your Lean workflow.

Read if Open only if you are debugging Lean or mathlib locally.

Open Semantic Scholar
7
negative score 0.8 2026-07-18 GitHub

leanprover-community/mathlib4: doc: fix the docstring of `IsTopologicalTorsor` (#41780)

Attila Gáspár

Recent commit on leanprover-community/mathlib4.

Why it matters Infrastructure signal: this may improve premise discovery, dependency retrieval, or library navigation for agents.

Skim cue Skim only the changed subsystem and whether it affects your Lean workflow.

Read if Open only if you are debugging Lean or mathlib locally.

Open Semantic Scholar

Source Health

Warnings are preserved so failed sources do not silently disappear from the brief.

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