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arxiv: 2606.05030 · v1 · pith:ZIDZJC7Bnew · submitted 2026-06-03 · 💻 cs.CL · cs.SC

Imbuing Large Language Models with Bidirectional Logic for Robust Chain Repair

Zehua Cheng , Wei Dai , Jiahao Sun , Thomas Lukasiewicz This is my paper

Pith reviewed 2026-06-28 05:44 UTC · model grok-4.3

classification 💻 cs.CL cs.SC
keywords chain-of-thought reasoningreasoning repairfill-in-the-middlebidirectional attentionsymbolic verificationlarge language modelserror snowballing
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The pith

TRI endows decoder-only LLMs with goal-conditioned infilling to repair erroneous segments in reasoning chains.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that autoregressive chain-of-thought reasoning fails when an early error corrupts all later steps because each token only conditions on what came before. TRI reframes the repair problem as a fill-in-the-middle task: given a verified prefix premise and a verified downstream milestone, the model must generate the missing logical bridge that connects them. It achieves this by rearranging input sequences into a Prefix-Suffix-Middle format with sentinel tokens so the causal architecture can attend to both ends without changing its attention mechanism. Training first uses supervised fine-tuning on verified triples from formal math, then direct preference optimization driven solely by a symbolic verifier. At test time the method acts as a targeted repair module that fixes only the broken segment while leaving correct parts untouched.

Core claim

By training on Prefix-Suffix-Middle sequences extracted from verified (P, S, M) triples and optimizing with a deterministic symbolic verifier as the sole reward signal, decoder-only transformers acquire the ability to synthesize logically sound bridges that connect a verified premise to a verified milestone, allowing surgical correction of reasoning chains without full regeneration.

What carries the argument

Prefix-Suffix-Middle (PSM) sequence rearrangement with three non-overlapping sentinel tokens that lets the middle infill attend to both the verified prefix and the verified suffix inside a standard causal self-attention stack.

If this is right

  • Only the damaged segment is regenerated while verified sections remain untouched.
  • Token consumption per problem drops by 31.2 percent on the tested benchmarks.
  • No LLM judge is required because the symbolic verifier supplies the sole reward signal.
  • State-of-the-art results are reported across all three evaluated benchmarks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same PSM rearrangement could be tested on non-mathematical domains if reliable external verifiers for those domains become available.
  • The method might be combined with other inference-time techniques such as self-consistency or tree search to further reduce error rates.
  • Extending the two-stage training to additional formal systems beyond Lean 4 and Python could broaden applicability.

Load-bearing premise

A deterministic symbolic verifier can always locate every error and confirm that the generated bridge is sound without introducing new mistakes or false negatives.

What would settle it

A concrete counter-example in which an infilled bridge passes the Lean 4 or Python verifier yet produces an incorrect final answer, or in which the verifier fails to flag a genuine error in the original chain.

Figures

Figures reproduced from arXiv: 2606.05030 by Jiahao Sun, Thomas Lukasiewicz, Wei Dai, Zehua Cheng.

Figure 1
Figure 1. Figure 1: Standard Chain-of-Thought (left) suffers from catastrophic error propagation: a single flawed step derails all subsequent reasoning. TRI (right) decouples logical mile￾stone discovery from gap-filling: a verified premise P and a future milestone S anchor a specialised infilling model that synthesises the missing bridge M under simultaneous bidirectional constraint, producing a symbolically verified complet… view at source ↗
Figure 2
Figure 2. Figure 2: System Architecture of TRI. The inference-time repair loop con￾sists of three components: (1) a causal draft model that produces an initial full￾length trace; (2) a deterministic symbolic verifier that locates the first logical fail￾ure at step k; (3) the TRI infilling model that, given the PSM-reordered input [Q,⟨premise⟩, P,⟨milestone⟩, S,⟨bridge⟩], autoregressively generates bridge M, con￾ditioned simul… view at source ↗
read the original abstract

Autoregressive chain-of-thought (CoT) reasoning in large language models (LLMs) is fundamentally forward-directed: each step conditions only on prior tokens. This unidirectional inductive bias renders even capable models susceptible to error snowballing, wherein a single logical or arithmetic mistake in an early step irreversibly corrupts the entire reasoning chain. We introduce Teleological Reasoning Infilling (\TRI{}), a training framework that endows decoder-only transformers with a native \emph{goal-conditioned bridging} capability. The key insight is to reframe erroneous reasoning segments as fill-in-the-middle (FIM) tasks: given a verified prefix premise $P$, a verified downstream milestone $S$, and the original query $Q$, the model must synthesise the logical bridge $M$ that connects $P$ to $S$ rigorously and completely. To achieve this with standard causal architectures, we introduce a Prefix-Suffix-Middle (PSM) sequence rearrangement with three non-overlapping sentinel tokens, enabling $M$ to attend to both $P$ and $S$ without any structural modification to the self-attention mechanism. Training proceeds in two stages: (i) Supervised Fine-Tuning (SFT) on symbolically verified $(P, S, M)$ triples extracted from formal mathematics corpora, and (ii) Direct Preference Optimisation (DPO) with a deterministic symbolic verifier (Lean 4 / Python) as the sole reward oracle, eliminating LLM-judge sycophancy. At inference, TRI operates as a surgical repair module within a dual-system loop: a causal draft model generates an initial trace, the verifier pinpoints failures, and TRI infills only the damaged segment, leaving verified sections intact. Comprehensive experiments on three benchmarks demonstrate that TRI achieves state-of-the-art performance across all tasks, while reducing per-problem token expenditure by 31.2%.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces Teleological Reasoning Infilling (TRI), a framework that trains decoder-only LLMs to perform goal-conditioned infilling for repairing errors in autoregressive chain-of-thought reasoning. By rearranging sequences into Prefix-Suffix-Middle format with sentinel tokens, the model learns to generate logical bridges M between verified premise P and milestone S. Training uses SFT on verified (P,S,M) triples from formal corpora followed by DPO using a symbolic verifier (Lean 4/Python) as reward oracle. At inference, a dual-system loop uses the verifier to detect failures and TRI to infill only damaged segments. The paper reports SOTA results on three benchmarks with 31.2% reduction in token expenditure.

Significance. If the results hold, the work offers a promising direction for mitigating error propagation in LLM reasoning by leveraging bidirectional context without architectural changes. The use of an external deterministic verifier for both reward and detection is a strength, as it avoids circular LLM-based judgments. This could have implications for reliable automated theorem proving and multi-step reasoning tasks. The parameter-free nature of the verifier-based training is also noteworthy.

major comments (2)
  1. [Abstract] Abstract: The abstract asserts SOTA performance across all tasks and a 31.2% token reduction, but provides no information on the three benchmarks, comparison baselines, statistical tests, or verification that gains survive verifier limitations, undermining the ability to assess the central claims.
  2. [Inference-time repair description] Inference-time repair description: The claim that the verifier 'pinpoints failures' and confirms the infilled bridge is 'logically sound' is load-bearing for the dual-system loop and token savings; however, no measurements of verifier recall, false-negative rates on test distributions, or handling of non-formalizable CoT errors are reported, leaving the robustness of the repair mechanism unverified.
minor comments (1)
  1. [Methods] The PSM sequence rearrangement is described but the exact placement of sentinel tokens and how they interact with the causal mask could be clarified with an example sequence.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback, which highlights important areas for improving the clarity and robustness of our claims. Below, we provide point-by-point responses to the major comments and outline the revisions we will implement.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The abstract asserts SOTA performance across all tasks and a 31.2% token reduction, but provides no information on the three benchmarks, comparison baselines, statistical tests, or verification that gains survive verifier limitations, undermining the ability to assess the central claims.

    Authors: We agree that the abstract would benefit from greater specificity to allow readers to evaluate the central claims. In the revised manuscript we will expand the abstract to name the three benchmarks, identify the primary baselines, reference the statistical tests used to establish significance, and note how the reported gains remain consistent under the documented constraints of the symbolic verifier. revision: yes

  2. Referee: [Inference-time repair description] Inference-time repair description: The claim that the verifier 'pinpoints failures' and confirms the infilled bridge is 'logically sound' is load-bearing for the dual-system loop and token savings; however, no measurements of verifier recall, false-negative rates on test distributions, or handling of non-formalizable CoT errors are reported, leaving the robustness of the repair mechanism unverified.

    Authors: The referee correctly notes the absence of explicit verifier performance metrics. While the verifier is deterministic on formalizable statements, we did not report recall or false-negative rates on the test distributions nor a systematic treatment of non-formalizable CoT errors. We will add a new subsection that discusses the verifier's scope and limitations, incorporates any empirical observations already available from our experiments, and clarifies how non-formalizable errors are currently handled or flagged. revision: partial

Circularity Check

0 steps flagged

No significant circularity; external verifier supplies independent signal

full rationale

The TRI framework's core training and inference loop uses a deterministic symbolic verifier (Lean 4 / Python) as the sole reward oracle for DPO and as the failure detector for segment isolation. This verifier operates outside the LLM and is not derived from or fitted to the model's own outputs, satisfying the criterion for independent evidence. No equations, self-definitional loops, fitted-input predictions, or load-bearing self-citations appear in the manuscript text. The SOTA performance and token-reduction claims rest on external benchmark experiments rather than any internal reduction of the target result to its own inputs by construction. The derivation is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on the standard transformer architecture and the assumption that symbolic verifiers can serve as perfect oracles; no new physical or mathematical entities are postulated.

axioms (1)
  • domain assumption Decoder-only transformers with causal attention can still attend bidirectionally to non-overlapping prefix and suffix segments when special sentinel tokens are inserted.
    Invoked to justify the PSM rearrangement without architecture changes.

pith-pipeline@v0.9.1-grok · 5878 in / 1304 out tokens · 22991 ms · 2026-06-28T05:44:09.763895+00:00 · methodology

discussion (0)

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Reference graph

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