Inference-Time Learning for LLMs: A Survey of In-Context, Retrieval-Augmented, and Test-Time Training

PhD Qualifying Examination


Title: "Inference-Time Learning for LLMs: A Survey of In-Context, 
Retrieval-Augmented, and Test-Time Training"

by

Miss Jinghan ZHANG


Abstract:

When deployed in real-world settings, language models often face target data 
that differ from their pretraining distribution - whether due to emerging 
knowledge, domain-specific styles or noisy inputs. Fine-tuning may be 
infeasible when compute is constrained, model weights are inaccessible, or 
only unlabeled test streams are available. To address this, we study 
inference-time learning: adapting model behavior during prediction to bridge 
the distribution gap and enhance reasoning under shift.

We organize inference-time learning along three axes: the learning signal 
(e.g., self-supervision, uncertainty-aware objectives, or verifiable 
feedback); the form of parameterization (ranging from contextual 
conditioning to lightweight adapters or external memory); and the temporal 
scale (single-shot adaptation, sliding-window updates, or online continual 
learning). Building on this taxonomy, we categorize existing algorithmic 
families - including in-context learning, retrieval-augmented reasoning with 
tool use, and test-time training and efficient finetuning - and analyze 
their trade-offs in stability, plasticity and compute efficiency.

To enable systematic evaluation, we systematize existing protocols and 
benchmark suites, clarifying distinctions between online and offline 
settings and among contextual input modalities (instructions, retrieved 
documents, programmatic scratchpads), while tagging learning content as 
knowledge, procedure, or calibration. We further discuss failure modes 
under distributional and semantic drift and outline metrics for performance, 
calibration, forgetting, regret, and explicit compute cost. Finally, we 
outline open challenges for safe long-horizon experience accumulation and 
robust deployment.


Date:                   Wednesday, 12 November 2025

Time:                   10:00am - 12:00noon

Venue:                  Room 5501
                        Lifts 25/26

Committee Members:      Dr. Junxian He (Supervisor)
                        Dr. Yangqiu Song (Chairperson)
                        Dr. May Fung