Anchoring failure mode refers to a critical degradation pattern in large language models where systems become locked onto early assumptions, initial errors, or wrong turns and subsequently fail to recover even when provided with corrective information in later conversation turns. This phenomenon represents a fundamental challenge in maintaining coherence and accuracy across extended interactions, particularly in multi-turn dialogue and agentic reasoning scenarios.

Anchoring failure mode describes the inability of language models to effectively override or correct initial premises once they have been established in a conversational context. Rather than treating new information as potentially corrective or superseding earlier statements, the model maintains commitments to earlier assumptions, creating a cascading effect where subsequent reasoning becomes constrained by these initial anchors ¹⁾.

The failure manifests across multiple temporal scales: at the token level, where immediate context biases generation; at the turn level, where earlier dialogue exchanges constrain later responses; and at the loop level, within iterative reasoning processes where initial problem decompositions lock the model into particular solution pathways. This multi-scale aspect distinguishes anchoring failure from simple context window limitations or attention mechanism issues ²⁾.

The anchoring failure mode is the underlying mechanism driving the broader lost in conversation phenomenon, where language models appear to forget, contradict, or lose track of established premises during extended interactions. Common manifestations include:

* Assumption Lock: Models establish an initial interpretation of a problem or statement and fail to revise it despite subsequent clarifications or contradictory information.

* Cascading Errors: An early mistake propagates through subsequent reasoning steps because the model treats the erroneous anchor as ground truth rather than a falsifiable assumption.

* Context Contradiction: Models generate responses that contradict information established in earlier turns, indicating failure to properly weight or integrate historical context against initial framings.

* Path Dependency: The particular way a problem is first presented creates a lock-in effect, preventing the model from exploring alternative interpretations even when explicitly prompted to do so ³⁾.

The anchoring failure mode reflects fundamental properties of transformer-based architectures and their training regimes. During inference, attention mechanisms weight tokens based on relevance to current predictions, which can create strong positional biases toward earlier tokens that establish key assumptions. These biases become difficult to override through subsequent context, particularly when the initial assumptions are semantically plausible and consistent with training data distributions ⁴⁾.

The phenomenon also connects to issues with model calibration and uncertainty representation. Rather than expressing doubt about initial premises when receiving contradictory information, models tend to maintain confidence in earlier statements and reinterpret new information to fit existing frameworks. This behavior may result from training objectives that reward coherent token sequences over accurate belief updating.

For agentic systems and multi-turn applications, anchoring failure mode represents a critical safety and reliability concern. Systems that cannot effectively revise their understanding based on new information may make increasingly confident incorrect decisions based on initial errors. This becomes particularly problematic in domains requiring iterative problem-solving, where each step should be capable of correcting or revising assumptions from previous steps ⁵⁾.

Mitigation strategies under investigation include explicit belief revision prompting, structured reasoning formats that separate assumption statements from derived conclusions, retrieval-augmented approaches that re-ground context in supporting evidence rather than relying solely on earlier turns, and training techniques that explicitly reward models for identifying and correcting contradictions within their own outputs.

• Lost in Conversation Phenomenon
• Lost in the Middle (Context Degradation)
• Aptitude vs Reliability Degradation in Multi-Turn