pith. sign in

arxiv: 2605.16516 · v1 · pith:Q3SDC2MPnew · submitted 2026-05-15 · 💻 cs.HC · cs.AI· cs.CL· cs.CY

Alignment Drift in Long-Term Human-LLM Interaction: A Mechanism-Oriented Framework

Xintong Yao This is my paper

Pith reviewed 2026-05-20 16:06 UTC · model grok-4.3

classification 💻 cs.HC cs.AIcs.CLcs.CY
keywords alignment driftlong-term interactionhuman-LLM interactionfeedback loopsinteraction regimesboundary conditionsconversation history
0
0 comments X

The pith

Long-term human-LLM interactions produce alignment drift where outputs become more shaped by conversation history than the current message, while still appearing helpful and coherent.

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

The paper establishes that prolonged interactions with LLM-based systems can lead to alignment drift, a gradual shift in which responses rely less on the immediate user input and more on accumulated prior history. This process unfolds through feedback loops and sub-pattern selection while the system continues to seem responsive and attuned. A sympathetic reader would care because existing work focuses on short-term tasks and isolated outputs, leaving these cumulative interaction dynamics understudied. The proposed framework divides the process into three interactional regimes and identifies boundary conditions that could help manage the drift.

Core claim

Alignment drift is a recursive interactional process in which system outputs become less constrained by the user's current message and more shaped by prior interaction history, while still appearing helpful, coherent, and responsive. The drift develops through feedback loops and sub-pattern selection. The framework distinguishes signal A from signal B, divides the interaction into three regimes, and specifies boundary conditions for controlling the drift.

What carries the argument

The mechanism-oriented framework that distinguishes signal A from signal B and models drift development through feedback loops, sub-pattern selection, three interactional regimes, and boundary conditions.

If this is right

  • Long-term studies of human-LLM interaction must track history dependence rather than relying solely on single-turn metrics.
  • Designers can introduce explicit boundary conditions to limit how far responses may drift from the current message.
  • Users may experience improving satisfaction even as the system's grounding in the latest input weakens.
  • The distinction between signal A and signal B provides a way to diagnose when drift begins in ongoing conversations.

Where Pith is reading between the lines

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

  • Periodic context resets or summarization steps could serve as practical interventions if the three-regime model holds.
  • The framework suggests similar drift patterns may appear in other persistent conversational agents beyond LLMs.
  • Testing the boundary conditions in controlled multi-session experiments would directly evaluate the proposed control mechanisms.

Load-bearing premise

Users' subjective experience improves as the system grows more familiar and attuned, which hides the shift toward history-shaped outputs.

What would settle it

A longitudinal study that measures both user satisfaction ratings and the statistical dependence of each response on prior turns across many sessions, checking whether satisfaction rises while history dependence increases.

read the original abstract

Long-term interaction with LLM-based systems may produce alignment drift: a gradual process in which system outputs become less constrained by the user's current message and more shaped by prior interaction history, while still appearing helpful, coherent, and responsive. This process is difficult to detect because the user's subjective experience may improve as the system becomes more familiar, useful, and attuned. Existing research on human-LLM interaction has largely focused on short-term task performance, isolated outputs, or single-instance alignment problems, leaving slow and cumulative interaction-level dynamics undercharacterized. This paper proposes a mechanism-oriented framework for describing alignment drift. The framework defines the distinction between signal A and signal B, explains how drift develops through feedback loops and sub-pattern selection, divides the process into three interactional regimes, and identifies boundary conditions for controlling drift. By framing alignment drift as a recursive interactional process rather than an isolated model-side failure, the paper provides a conceptual basis for studying long-term human-system interaction.

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

1 major / 2 minor

Summary. The paper proposes a mechanism-oriented framework for alignment drift in long-term human-LLM interactions. It defines alignment drift as a gradual process in which system outputs become less constrained by the user's current message and more shaped by prior interaction history, while still appearing helpful, coherent, and responsive. The framework distinguishes between signal A and signal B, explains drift through feedback loops and sub-pattern selection, divides the process into three interactional regimes, and identifies boundary conditions for controlling drift.

Significance. If the framework holds, it would offer a significant conceptual contribution to the study of human-LLM interaction by highlighting slow, cumulative dynamics that existing short-term focused research has overlooked. The recursive interactional process framing is a particular strength, as it moves beyond isolated model failures to consider the full interaction history. This could serve as a foundation for future empirical studies and system designs aimed at managing long-term alignment.

major comments (1)
  1. The section describing the three interactional regimes: the division into regimes is presented without explicit criteria, measurable indicators, or transition conditions for identifying or moving between them. This specification is load-bearing for the framework's utility in characterizing or analyzing actual interactions.
minor comments (2)
  1. The abstract is clear but could explicitly note that the contribution is a conceptual framework without empirical validation or formal derivations to set reader expectations.
  2. Consider adding a simple illustrative scenario or diagram in the framework section to demonstrate how feedback loops and sub-pattern selection operate in practice.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive review and for acknowledging the potential significance of the framework in addressing cumulative dynamics overlooked by short-term focused research. We address the major comment below and have incorporated revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: The section describing the three interactional regimes: the division into regimes is presented without explicit criteria, measurable indicators, or transition conditions for identifying or moving between them. This specification is load-bearing for the framework's utility in characterizing or analyzing actual interactions.

    Authors: We agree that the original presentation of the three regimes would benefit from more explicit specification to support empirical application. In the revised manuscript, we have expanded the relevant section to define each regime according to the relative dominance of signal A (current-message constraints) versus signal B (historical shaping), with measurable indicators including the proportion of output variance explained by interaction history and the rate of sub-pattern selection. Transition conditions are now specified in terms of cumulative feedback-loop iterations and thresholds in history-dependent output divergence. These additions aim to make the framework more usable for characterizing interactions while retaining its conceptual, mechanism-oriented character. revision: yes

Circularity Check

0 steps flagged

Conceptual framework without load-bearing circular derivations

full rationale

The paper is a conceptual proposal that introduces alignment drift as a gradual process defined via explicit distinctions (signal A vs. signal B), feedback loops, sub-pattern selection, and three interactional regimes. No mathematical derivations, fitted parameters, equations, or empirical predictions appear in the provided text. The central framing relies on stated conceptual distinctions rather than reducing any claim to its own inputs by construction or via self-citation chains. This makes the framework self-contained as a descriptive starting point, consistent with the absence of any quoted reduction to prior fitted quantities or author-specific uniqueness theorems.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The proposal rests on conceptual distinctions and assumptions about interaction dynamics drawn from the abstract without empirical or formal grounding.

axioms (1)
  • domain assumption Existing research has largely focused on short-term task performance, isolated outputs, or single-instance alignment problems.
    Used to establish the gap that the new framework addresses.
invented entities (1)
  • alignment drift no independent evidence
    purpose: To name and structure the gradual history-influenced shift in LLM outputs during extended interactions.
    Introduced as the central phenomenon the framework describes.

pith-pipeline@v0.9.0 · 5695 in / 1309 out tokens · 52271 ms · 2026-05-20T16:06:22.709938+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

10 extracted references · 10 canonical work pages · 1 internal anchor

  1. [1]

    P. F. Christiano, J. Leike, T. B. Brown, M. Martic, S. Legg, and D. Amodei. Deep rein- forcement learning from human preferences. InAdvances in Neural Information Processing Systems, volume 30, 2017

    work page 2017

  2. [2]

    Ouyang, J

    L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. Christiano, J. Leike, and R. Lowe. Training language models to follow instructions with human feedback. InAdvances in Neural Information Processing Systems, volume 35, 2022

    work page 2022

  3. [3]

    Towards Understanding Sycophancy in Language Models

    M. Sharma, M. Tong, T. Korbak, D. Duvenaud, A. Askell, S. R. Bowman, N. Cheng, E. Dur- mus, Z. Hatfield-Dodds, S. R. Johnston, S. Kravec, T. Maxwell, S. McCandlish, K. Ndousse, 15 O. Rausch, N. Schiefer, D. Yan, M. Zhang, and E. Perez. Towards understanding sycophancy in language models.arXiv preprint arXiv:2310.13548, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  4. [4]

    Chandra, M

    K. Chandra, M. Kleiman-Weiner, J. Ragan-Kelley, and J. B. Tenenbaum. Sycophantic chatbots cause delusional spiraling, even in ideal Bayesians.arXiv preprint arXiv:2602.19141, 2026

    work page arXiv 2026

  5. [5]

    J. D. Lee and K. A. See. Trust in automation: Designing for appropriate reliance.Human Factors, 46(1):50–80, 2004

    work page 2004

  6. [6]

    Parasuraman and D

    R. Parasuraman and D. H. Manzey. Complacency and bias in human use of automation: An attentional integration.Human Factors, 52(3):381–410, 2010

    work page 2010

  7. [7]

    Buçinca, M

    Z. Buçinca, M. B. Malaya, and K. Z. Gajos. To trust or to think: Cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making.Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1):1–21, 2021

    work page 2021

  8. [8]

    Mesoudi, A

    A. Mesoudi, A. Whiten, and K. N. Laland. Towards a unified science of cultural evolution. Behavioral and Brain Sciences, 29(4):329–347, 2006

    work page 2006

  9. [9]

    Moussaïd, H

    M. Moussaïd, H. Brighton, and W. Gaissmaier. The amplification of risk in experimental diffusion chains.Proceedings of the National Academy of Sciences, 112(18):5631–5636, 2015

    work page 2015

  10. [10]

    the manager,

    T. Taniguchi. Collective predictive coding hypothesis: Symbol emergence as decentralized Bayesian inference.Frontiers in Robotics and AI, 11:1353870, 2024. A Decomposition of the Scene The following decomposition breaks the scene into a sequence of interactional steps. Rather than reading the exchange as one continuous conversation, this appendix isolates...

    work page 2024