Learning Is Selective Change
Hidden gems found by random luck in the ICML 2026 workshops
Main-conference poster sessions reward planning. Workshop sessions reward luck.
A workshop room can be three corridors away from the topic you intended to see, and the poster that changes your afternoon may be the one beside the coffee table rather than the one highlighted in the program. This third ICML reflection is limited to workshop papers I encountered through that kind of fortunate wandering.
The papers span theoretical physics, post-training, foundation models for simulation, Transformer inductive bias, model evaluation, interpretability, and multi-agent diagnosis. Their common question emerged only after I stopped grouping them by application:
When an AI system encounters an error or a new environment, what should change, where should that change be stored, and what evidence tells us that the change was useful?
Learning is often described as parameter optimization. These workshop papers suggest a broader view. An AI system can change its current answer, its dialogue state, its inferred model of the environment, a selected subspace of its weights, or the interaction protocol among several agents. Every update preserves some structure and overwrites something else.
A useful update therefore balances three quantities:
\[U^\star = \arg\min_U \mathcal{L}*{\mathrm{new}}(U(s)) + \lambda \mathcal{D}*{\mathrm{preserve}}(U(s),s) + \mu \mathcal{C}(U).\]Here, (s) is the current state of the system, (\mathcal{L}{\mathrm{new}}) measures the unresolved error, (\mathcal{D}{\mathrm{preserve}}) measures damage to valuable existing structure, and (\mathcal{C}) represents the cost of the update.
The hard research problem lies in defining those terms. A critique can improve the current derivation while making the overall argument less coherent. Fine-tuning can raise benchmark accuracy while erasing general capabilities. A physics model can produce a plausible next frame while learning the wrong dynamics. A lower perplexity can reward confidence without rewarding correctness. A group of agents can agree because they share the same error.
The workshop papers became a study of selective change: changing the right variable, on the right timescale, while preserving the right invariants.
Critique changes the solution before it changes the model
When Does Critique Improve AI-Assisted Theoretical Physics? SCALAR studies an Actor–Critic–Judge loop on graduate-level quantum field theory and string theory problems. An Actor proposes and revises a solution, a Critic provides feedback with access to a reference solution, and an independent Judge evaluates the successive attempts. The authors vary Actor personas, Critic styles, model families, and model scales. Multi-turn interaction generally improves on single-shot attempts, while the size and character of the gain depend strongly on the Actor–Critic pairing. Constructive feedback is particularly useful when a weaker Actor is paired with a stronger Critic; strict or adversarial feedback provides no universal advantage. (arXiv)
The important object is the response of the solution to feedback:
\[G_t = S_{t+1} - S_t,\]where (S_t) is the quality of the solution after revision (t). A capable Critic can identify the correct error and still produce little gain when the Actor cannot interpret or incorporate the feedback. A milder Critic may outperform a more aggressive one by locating a repair within the Actor’s current competence.
This resembles educational scaffolding more than debate. A useful teacher does not enumerate every flaw simultaneously. The teacher identifies the next misconception that the student can repair. The value of feedback therefore depends on its correctness, novelty, timing, and usability.
SCALAR also offers an instructive evaluation method: track the sequence of revisions rather than only the final score. Two systems with identical final performance can have very different internal dynamics. One may repair a decisive error after a single critique. Another may oscillate among alternative mistakes. A third may repeatedly rewrite the answer while changing little of substance.
For scientific agents, this suggests measuring critique susceptibility: how efficiently a system converts feedback into improved work. When gains flatten, the appropriate response may be to switch Critics, decompose the problem, invoke a symbolic tool, or ask for human input. Continuing the same dialogue merely generates more tokens.
The broader lesson is that many failures should be repaired in the current artifact before they trigger a model update. A wrong sign in a derivation calls for a local correction. A systematic inability to reason about gauge invariance may justify a deeper change. The skill lies in distinguishing the two.
Context can carry learning without changing weights
Towards a Physics Foundation Model moves adaptation from dialogue into latent state estimation. Its General Physics Transformer is trained on approximately 1.8 TB of simulation data covering several fluid, thermal, multiphase, and fluid–solid systems. Given a short history of physical states, the model infers enough of the local dynamics to predict future states, including zero-shot experiments on unseen physical systems and boundary conditions. (arXiv)
The task is naturally expressed through a latent dynamics variable (z):
\[p(u_{t+1} \mid u_{t-k+1:t}) = \int p(u_{t+1} \mid u_t,z) p(z \mid u_{t-k+1:t}),dz.\]The model observes a short trajectory, infers a local description of how this world evolves, and uses that inferred state to generate the next step. The weights remain fixed while the effective model changes through context.
This is closely related to system identification in control theory. An engineer observes inputs and outputs, estimates the hidden dynamics, and then predicts or controls the system. A physics foundation model amortizes that procedure across many simulated worlds.
The architecture also contains an important scientific design choice. The Transformer estimates a temporal derivative, while a numerical integration step advances the state. Spatial and temporal derivative features provide additional numerical structure. The learned component handles cross-domain inference; the integration interface supplies a stable computational scaffold. (arXiv)
This hybrid structure is more inspiring than a slogan about learning physics from raw data. It suggests that general scientific models may arise from broad learned representations connected through well-chosen numerical interfaces. A neural component can infer the regime, parameters, or operator, while established solvers enforce part of the update structure.
Variable time increments are another methodological gem. When all trajectories share the same sampling interval, visual displacement can become a shortcut for temporal dynamics. Varying the interval forces the model to infer time scale from the context. Similar experiments could be useful in language-model research: change surface format, naming, scale, or tokenization while preserving the underlying operation, then test whether the model infers the mechanism or recognizes the dataset.
The central unresolved question is what kind of latent model has been inferred. The context may identify a familiar simulation family, support a flexible local interpolation, or recover reusable abstractions such as boundary type, conservation structure, or effective dynamics. Carefully constructed pairs of visually similar trajectories governed by different hidden laws could distinguish these explanations.
For AI research agents, the analogy is direct. A mechanistic-interpretability agent should infer a local intervention law from a small set of experiments. It should estimate thresholds, dose–response shape, generalization across prompts, and failure of perturbative assumptions before proposing the next intervention. Most new experimental objects should be handled through contextual system identification long before they motivate weight updates.
Fine-tuning should protect the structure that future learning needs
Rotation-Preserving Supervised Fine-Tuning studies selective change in parameter space. Standard SFT improves the target task while often reducing out-of-domain performance. RPSFT penalizes changes within dominant singular subspaces of the pretrained weight matrices, using those subspaces as an efficient proxy for directions that are expensive to disturb. The complementary space remains available for task adaptation. Across math-reasoning fine-tuning experiments, the method improves the in-domain/OOD trade-off, better preserves pretrained representations, and produces stronger initializations for later reinforcement learning. (arXiv)
The method embodies the stability–plasticity dilemma from continual learning and neuroscience. A system needs enough plasticity to acquire a new capability and enough stability to retain its reusable organization. Uniformly constraining the entire model wastes available degrees of freedom. Unconstrained adaptation allows a local training objective to rotate broadly useful machinery.
RPSFT asks a more precise question: which subspaces should carry the new task?
The dominant pretrained directions are a structural proxy rather than a complete map of capability. Their value comes from being computationally tractable and empirically associated with Fisher-sensitive gradient energy. The methodological pattern is broadly reusable: identify an expensive behavioral object, propose a cheaper structural proxy, verify that the proxy captures the relevant signal, and constrain only the corresponding degrees of freedom.
The downstream reinforcement-learning result deserves special attention. A checkpoint can look good immediately after SFT and provide a poor starting point for the next training stage. Preserving pretrained geometry can improve the future learning trajectory even when the current checkpoint difference looks modest.
This expands the meaning of update quality. A good adaptation should solve the present task, preserve unrelated capabilities, and maintain useful plasticity for future stages.
For alignment research, the next question concerns which behavioral mechanisms occupy the protected space. Refusal, truthfulness, self-report, and reasoning may share dominant pretrained geometry, or they may rely on fragile structures introduced later through post-training. Average OOD retention cannot distinguish these possibilities. Causal and representational measurements across ordinary SFT and RPSFT could reveal which alignment mechanisms survive because their supporting subspaces remain stable.
Weight space can become an observable
Weight-Space Physics offers the most unusual interpretability idea in this workshop collection: treat trained network weights as scientific measurements of the system they model.
The paper studies normalizing flows trained for lattice field theories across different coupling constants. JEPAWG maps physical couplings through a learned latent space into flow-network weights. The latent weight geometry recovers the intrinsic dimensionality of the coupling manifold, identifies the phase-transition region, and exhibits a finite-size shift aligned with the expected two-dimensional Ising scaling exponent. The model also generates effective flow weights at unseen coupling values. (arXiv)
Normally, a learned sampler is a tool for producing configurations from which physical observables are estimated. This work asks whether the parameters of the tool have themselves absorbed the structure of the physical theory.
That reversal creates an unusually clean interpretability benchmark. Physics provides known coupling dimensions, phase structure, and finite-size scaling. An interpretability method can therefore be tested against independently established ground truth rather than judged by whether its visualizations appear meaningful.
The cross-disciplinary inspiration is statistical physics. A phase transition captures a qualitative change in macroscopic behavior. An order parameter summarizes a regime. Finite-size scaling helps distinguish a genuine structural transition from a small-system artifact. Renormalization describes relationships preserved across scales.
Model-family interpretability could borrow the same logic. Consider a controlled family of language models trained with varying amounts of safety data, preference strength, harmful fine-tuning, or reasoning supervision. The checkpoints may form a low-dimensional weight-space manifold. Behavioral transitions could appear as geometric changes along that manifold, while scaling across model sizes could test whether the transition reflects stable structure.
This shifts interpretability away from isolated checkpoints. A single neuron in a single model can be an implementation detail. A consistent manifold, transition boundary, or scaling law across a controlled model family provides a stronger scientific object.
The paper also complicates the usual distinction between learning and measurement. Training a network at each physical coupling creates a measurement instrument whose parameters reflect the underlying system. The hypernetwork then learns the geometry of those measurements. Weight space becomes a bridge between simulation, representation learning, and scientific inference.
Inductive bias determines which differences survive learning
Transformers Learn Low Sensitivity Functions studies the kinds of functions Transformers tend to learn. Across vision and language experiments, the authors find that Transformers learn functions with lower sensitivity to token- or patch-level random perturbations than several alternative architectures. Lower sensitivity correlates with robustness, flatter loss regions, and progress during grokking. (arXiv)
For a Boolean function, sensitivity can be written as
\[\operatorname{Sens}(f) = \mathbb{E}*x \sum*{i=1}^{n} \mathbf{1}[f(x) \neq f(x^{\oplus i})].\]A low-sensitivity function maps many neighboring inputs to the same output. The model compresses away local distinctions that do not appear necessary for its learned rule.
This bias explains part of Transformer robustness. It also creates a boundary between useful invariance and harmful blindness. In sentiment classification, one irrelevant token should rarely change the answer. In parity, exact arithmetic, source code, or cryptographic verification, a single symbol can determine correctness.
A follow-up line of research shows how simplicity bias can trap Transformers in an incorrect low-sensitivity rule under noisy features, even when the intended target is learnable. The architecture’s preference becomes helpful only when its favored invariances match the causal structure of the task. (arXiv)
This connects to the broader theme of selective change. Learning requires the model to decide which input differences deserve a different output. Low sensitivity says that Transformers often require stronger evidence before introducing a distinction.
Average sensitivity can still hide sparse high-leverage directions. A safety behavior may be stable under thousands of random edits and flip under one authority cue, role token, encoding convention, or activation direction. This motivates conditional sensitivity measurements targeted at task-relevant transformations rather than isotropic noise.
The architecture should ignore nuisance variation and remain responsive to causal variation. Determining which is which remains a property of the task, the data, and the measurement procedure.
A learning system can optimize the wrong evidence
Perplexity Cannot Always Tell Right from Wrong asks whether the metric used to judge learning preserves the ordering that actually matters.
In a simplified binary setting with accuracy (a) and confidence (\gamma), the average negative log-likelihood is
\[\mathcal{L}(a,\gamma) = -a\log \gamma - (1-a)\log(1-\gamma).\]Equal-perplexity curves in the accuracy–confidence plane show that a more accurate model can receive worse perplexity when confidence changes in the wrong proportion. The paper also uses continuity properties of compact decoder-only Transformers to prove the existence of sequences that the model predicts incorrectly while assigning very low perplexity. (arXiv)
A structurally decisive error can disappear inside an average over many easy token predictions. The model may copy almost an entire sequence confidently and fail at the one position that determines task success. Perplexity remains low because the error occupies little of the total sequence mass.
The result becomes more consequential when the same blind spot affects gradients. A metric that fails to reveal an error during evaluation may also provide little signal for correcting it during learning.
This paper supplies the logical foundation beneath the previous workshop results. Critique helps only when the Critic identifies the true error. Fine-tuning helps only when the objective values the capabilities we intend to preserve. A physics model learns useful dynamics only when one-step loss and rollout evaluation reward the right structure. An interpretability manifold means something only when the measured geometry corresponds to the physical or behavioral quantity of interest.
The cross-disciplinary connection is decision theory. Log-likelihood is a principled scoring rule for distribution estimation, while deployments usually assign highly unequal utility to different mistakes. One wrong tool call, medical diagnosis, proof step, or safety decision can dominate thousands of locally plausible tokens.
A useful metric must therefore match the structure of the claim. Exact reasoning needs exact or verifier-based outcomes. Physical simulation needs rollout stability and invariant preservation. Agent evaluation needs action correctness and consequence-weighted cost. Average likelihood remains informative, while its interpretation must stay tied to the task.
Agreement can preserve a shared mistake
When Agreement Becomes Unsafe studies multi-agent deliberation in diagnostic-style decisions and starts from a crucial observation: agreement does not certify safety. Agents can converge because they share evidence, reasoning, or bias. The work introduces loss-aware energy control so that the deliberation policy accounts for disagreement, asymmetric decision costs, and the risk of consensus on a costly alternative. The paper appeared as an oral at the ICML 2026 Workshop on Failure Modes of Agentic AI. (OpenReview)
This complements SCALAR. Critique studies whether one agent can improve another’s solution. Diagnostic deliberation asks whether interaction should continue, stop, or escalate when several agents appear to agree.
Consensus is valuable when errors are sufficiently independent and agreement reflects converging evidence. Consensus becomes dangerous when all agents inherit the same training bias, missing feature, or misleading observation. Adding more similar agents can increase confidence without adding information.
The correct mediator must reason about the loss landscape over possible decisions. In high-stakes classification, false positives and false negatives can have different consequences. An agreement on the costly error may deserve more scrutiny than a mild disagreement among low-cost alternatives.
The cross-disciplinary analogy comes from ensemble decision-making and safety-critical diagnosis. A panel of identical instruments does not create independent evidence. Diversity in model name, prompt wording, or sampling seed may leave the relevant blind spot untouched.
The design question is therefore deeper than how many agents should vote. A reliable system needs to know which evidence each agent contributes, which biases they share, and which outcomes justify abstention or escalation.
The center: learning across timescales
These workshop papers describe changes occurring at several levels.
SCALAR updates a current solution through interaction. A physics foundation model updates a latent description of the environment through context. RPSFT changes selected parts of weight space while protecting reusable structure. Weight-Space Physics studies the geometry created by whole families of trained models. Low-sensitivity research identifies an architectural bias governing which input distinctions survive learning. Perplexity analysis examines whether the objective can recognize improvement. Loss-aware deliberation asks whether a group’s agreement is trustworthy enough to act upon.
The timescales differ, yet the design questions remain aligned.
A local error should first be repaired in the current artifact. A changed environment may require a new latent model. A repeated capability gap may justify a weight update. A weight update should preserve the structure needed for generalization and future learning. Every level requires a metric that sees the consequential error, and every multi-agent system needs a way to distinguish new evidence from duplicated confidence.
This produces a hierarchy of adaptation:
\[\text{artifact revision} \rightarrow \text{contextual inference} \rightarrow \text{weight update} \rightarrow \text{model-family redesign}.\]Moving right increases cost and persistence. A capable AI system should use the cheapest level that can resolve the error while escalating when the lower level has reached its limit.
This framing also changes how I think about scientific AI. A research agent should not immediately fine-tune itself whenever an experiment disagrees with its prediction. It should first revise the derivation, update the local hypothesis, inspect the measurement, and run a discriminating experiment. Weight updates belong to capability gaps that persist across many contexts.
The workshops offered a quieter message than the main poster halls. Better intelligence may depend less on making every component stronger and more on assigning change to the correct place.
Learning is the art of changing the variables that caused the error while preserving the structure that will still be needed afterward.
Finding these papers required some luck. Recognizing that they were asking the same question required walking away from their application labels.
References
Rotation-Preserving Supervised Fine-Tuning
Towards a Physics Foundation Model
Perplexity Cannot Always Tell Right from Wrong
Weight-Space Physics: Interpretable Hypernetworks for Lattice Quantum Field Theories
Transformers Learn Low Sensitivity Functions: Investigations and Implications
When Agreement Becomes Unsafe: Loss-Aware Energy Control for Diagnostic Deliberation